Manual Pressure Techniques Activate Descending Pain-Modulatory Pathways and Reduce Headache Intensity in Chronic Tension-Type Headache: A Randomized Crossover Trial

Manual Pressure Techniques Activate Descending Pain-Modulatory Pathways and Reduce Headache Intensity in Chronic Tension-Type Headache: A Randomized Crossover Trial | 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 Manual Pressure Techniques Activate Descending Pain-Modulatory Pathways and Reduce Headache Intensity in Chronic Tension-Type Headache: A Randomized Crossover Trial Bram te Molder, Xianhua Zeng, Bob van den Meiracker, Gwendolyne Scholten-Peeters, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9222006/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Chronic tension-type headache (CTTH) is characterized by central sensitization and impaired descending pain modulation. Manual pressure techniques are hypothesized to engage descending pain-modulatory pathways, a mechanism quantifiable through conditioned pain modulation protocols. We investigated whether manual pressure techniques activate these pathways and compared their efficacy with the cold pressor test. Methods In this randomized crossover trial, thirty-seven participants with CTTH received three conditions: manual pressure techniques, sham techniques, and the cold pressor test. Primary outcomes were pressure pain thresholds at the trapezius and tibialis anterior muscles, and headache intensity. Data were analyzed using linear mixed-effects models. Secondary analysis examined the association between conditioned pain modulation responder status and clinical outcomes. An exploratory analysis was performed to assess effect modification. Results Both manual pressure techniques and the cold pressor test significantly increased global pressure pain thresholds compared to sham (p < 0.002). There was no significant difference between threshold increases between manual pressure techniques and the cold pressor test (p = 0.96), suggesting comparable activation of modulatory mechanisms. Notably, only manual pressure techniques resulted in a significantly greater reduction in headache intensity compared to both the sham and the cold pressor test (p 0.59). Responder status was significantly associated with outcomes: responders demonstrated a larger pressure pain threshold increase (p = 0.039) and superior headache relief (p = 0.049) than non-responders. Exploratory analyses identified male sex and absence of analgesic medication as relevant effect modifiers. Conclusions Manual pressure techniques reduce pain sensitivity, indicating activation of descending pain-modulatory pathways in CTTH, with effects comparable to those observed with the cold pressor test. However, the unique efficacy of manual pressure techniques in reducing headache intensity suggests clinical benefits beyond generalized noxious inhibitory control. These findings support manual pressure techniques as a mechanism-informed intervention for CTTH. chronic tension-type headache conditioned pain modulation inhibitory control central sensitization manual pressure techniques Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Tension-type headache (TTH) is the most prevalent type of primary headache. Chronic tension-type headache (CTTH), defined as having more than 15 headache days per month over a period of more than three months, affects between 0.5% and 4.8% of the global population ( 1 ) and has a substantial impact on quality of life ( 2 ). The pathophysiological mechanisms of CTTH are not fully understood, although current literature suggests central pain mechanisms play a dominant role ( 1 – 3 ). The trigeminal cervical complex (TCC) with connections to the upper cervical spine is considered a critical pathway due to its pivotal role in the development of TTH ( 4 ). Referred pain from the upper cervical spine to the head can be elicited through these pathways by applying pressure or stretch to cervical structures such as muscles, ligaments, and joints ( 5 – 7 ), a process that is reinforced by central and peripheral sensitization ( 1 , 8 ). Given the involvement of these pathways and the frequent co-occurrence of neck pain in CTTH, dysfunctions of the upper cervical spine, including muscles and joints, such as pericranial tenderness and heightened sensitivity to pressure pain, are commonly observed in patients with CTTH and may contribute to its development, maintenance, or exacerbation ( 2 , 9 , 10 ). Research shows that manual therapy can reduce headache frequency and intensity, particularly when combined with exercise interventions ( 11 – 13 ). Manual therapy comprises a range of clinician-applied, hands-on techniques used in the assessment and treatment of musculoskeletal disorders. Commonly used hands-on interventions include manual pressure techniques (MPTs), which apply pressure to specific areas of the upper cervical spine to reduce pain ( 12 , 14 ). Although MPTs are frequently used in clinical practice, a clear mechanistic understanding of MPTs remains elusive. A better understanding of these mechanisms is essential for developing more effective and personalized treatment strategies. Spinal mobilizations have been shown to activate descending pain modulation mechanisms mediated by the central nervous system ( 15 ), a process that may also apply to MPTs, as observed in healthy pain-free individuals ( 16 , 17 ). One method to measure descending pain modulation is through conditioned pain modulation (CPM), also known as the pain-inhibits-pain effect, in which a noxious stimulus applied at one body site inhibits pain and nociception at another, contralateral site ( 18 – 20 ). Descending pain modulation mechanisms are typically evaluated in clinical research using a noxious cold stimulus, such as the cold pressor test (CPT). These mechanisms involve key brainstem structures, such as the periaqueductal grey and the rostral ventral medulla, which regulate nociceptive transmission via endogenous opioid pathways ( 21 ). Reduced CPM efficiency has been associated with higher pain sensitivity in various chronic pain conditions ( 18 , 19 ), including chronic headaches ( 22 , 23 ). The current findings support the hypothesis that MPTs may influence sensory processing by stimulating descending pain modulation mechanisms. Therefore, we hypothesize that engagement of these descending pain-modulatory mechanisms contributes to headache reduction in individuals with CTTH. To effectively isolate the specific neurophysiological mechanisms of MPTs, it is necessary to distinguish the actual treatment effects from non-specific treatment effects, such as placebo responses and contextual factors (e.g., patient expectations). This distinction requires the inclusion of a sham-control group ( 24 ). Furthermore, to specifically test the hypothesis that MPTs engage descending pain modulation, we compare their effects with those of a standardized CPM-activating conditioning stimulus, namely the CPT. Consequently, this study aims to investigate 1) the CPM effects of MPTs applied to the upper cervical spine on pressure pain thresholds (PPTs) and 2) to explore their effects on headache intensity in people with CTTH, comparing these outcomes to both sham MPT and CPT. 2 Methods 2.1 Study design This randomized crossover trial was conducted in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines for randomized crossover trials ( 25 ). Participants were randomly assigned to one of six possible sequences, comprising three conditions: MPTs, sham MPTs, and CPT. The study protocol was approved by the Ethics Committee of the Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam (VCWE-2025-015). 2.2 Participants Participants were recruited from general practitioners and a regional network of physiotherapy practices specializing in headache from February 2025 to July 2025. Eligible participants were screened by a researcher for CTTH according to ICHD-III criteria, were aged 18–65, and were able to read either Dutch or English. Participants were excluded if they had a diagnosis of secondary headache (e.g., medication overuse headache), used muscle relaxants, had clinically diagnosed depression and/or anxiety disorders, hypersensitivity to cold stimuli, a history of significant cervical trauma or cervical surgery, or were pregnant. To minimize potential effects on CPM, participants were asked to refrain from consuming caffeine and alcohol for 24 hours before the assessments ( 26 , 27 ). All participants provided written informed consent before inclusion. 2.3 Randomization and blinding Participants were randomly assigned to a computerized allocation sequence that included MPT, sham MPT, and CPT. This randomization generated six possible permutations of the three condition groups. After randomization, a researcher instructed the clinicians to apply the three conditions in the specified order. The clinicians were unaware of the participants’ characteristics. To maintain blinding of outcome assessment, an independent examiner conducted all PPT measurements before and after each condition. This examiner remained consistent across all participant measurements and sessions and was blinded to the condition allocation. Data from all measurements were recorded by a research assistant who was consistently present in the room. Participants were blinded to the allocation of conditions. Clinicians applying the conditions could not be blinded due to the study’s nature. Furthermore, the statistician responsible for the data analysis remained blinded to the condition allocation. Data were provided in a coded format to ensure unbiased processing. 2.4 Procedures Participants were assessed in a single session at the same location. The total duration of the conditions and measurements was approximately 60 minutes. The experimental procedure consisted of four phases: familiarization, baseline measurement including the pre-test stimulus (pressure pain thresholds (PPTs)), conditioning stimulus (CPT, MPT, or sham MPT), and post-test stimulus (PPTs). The order of these phases was different between groups. Each condition session was separated by a 20-minute washout period to minimize carry-over effects ( 27 ). Measurements within the protocol adhered to a sequential CPM design. After randomization, participants received either MPT, sham MPT, or CPT (period 1). After a 20-minute washout period, they proceeded to a second condition (period 2), followed by another 20-minute washout period and a final condition (period 3). The standardized pre- and post-condition assessment included headache intensity measurement (using the Numeric Pain Rating Scale, NPRS) and PPTs. During each washout period, participants completed the questionnaires addressing perceived limitations due to headaches and pain catastrophizing. The overall procedure is illustrated in Flowchart 1. Flowchart 1 Study procedure. MPT = manual pressure technique; CPT = cold pressor test; PPT = pressure pain threshold; NPRS = numeric pain rating scale. 2.5 Conditioning stimuli 2.5.1 Cold pressor test The CPT was used to measure the CPM effect. The CPT is the most commonly used and widely accepted CS in CPM measurements ( 19 , 28 ). The CPT procedure involved immersing the participant's non-dominant hand, up to the wrist, in a cold-water bath equipped with a circulating pump, maintained at 10–12°C. The immersion lasted 60 seconds, during which the research assistant recorded the participant’s pain intensity every 20 seconds using the NPRS ( 29 ). 2.5.2 Manual pressure technique The MPTs were performed by experienced, trained musculoskeletal physiotherapists with expertise in headache and cervical spine disorders. The MPT was administered with both thumbs placed on the participant’s suboccipital muscles on the non-dominant side, while the participant remained in a prone position with the cervical spine maintained in a neutral position. The assessor gradually increased pressure to achieve a pain score of at least 5–6 on the NPRS. A timer was started, and the same pressure was maintained for a maximum of 120 seconds. NPRS scores were recorded every 20 seconds by the research assistant, as previously described by de Hertogh et al. in 2022 ( 5 ). 2.5.3 Sham manual pressure technique The sham MPT procedure was designed identically to the active MPT condition. However, only minimal pressure was applied to ensure that no pain was provoked. This light pressure was maintained for 120 seconds and repeated three times, following the exact timing and procedure as active MPTs. 2.6 Test stimulus 2.6.1 Pressure pain thresholds PPTs were applied as the test stimulus (TS). PPTs are widely used and considered as a reference standard for assessing CPM effects ( 19 , 28 ). PPTs were assessed using a calibrated digital pressure algometer (Type II, Somedic Electronics, Solna, Sweden) with a 1 cm² probe and expressed in kilo-pascals (kPa). Measurements were conducted before and immediately after each condition at two body sites on the participant’s dominant side: the midpoint of the trapezius and the anterior tibialis muscle, while the participants remained in a prone position. The pressure was applied perpendicularly at a rate of 50 kPa/s until the feeling of pressure changed into pain. At that point, they were instructed to press the button and stop the measurement. The value displayed on the algometer at that moment was recorded. PPT measurements were performed three times at each site, and the mean score was used in the analysis. A 20-second interval was maintained between measurements to prevent temporal summation or wind-up phenomena ( 27 ). The Somedic algometer has shown excellent reliability, with intrarater reliability coefficients ranging from 0.90 to 0.95 in healthy participants and from 0.89 to 0.96 in individuals with migraine ( 9 ). 2.7 Questionnaires The NPRS was used to assess self-reported headache intensity at the start and end of every period. Participants were asked to rate their current pain level on an 11-point scale, ranging from 0 (no pain) to 10 (the worst pain imaginable). The NPRS has demonstrated good intrarater reliability (r = 0.72) in participants with headaches ( 30 ). The Headache Impact Test (HIT-6) measures the impact of headaches across various domains, including work, social life, and cognitive function. Total scores range from 36 to 78, with higher scores indicating greater impact, categorized into four severity levels. The Dutch version has demonstrated psychometric equivalence and good reliability (intrarater reliability coefficient 0.78–0.90) in people with headache ( 31 ). Pain catastrophizing was assessed using the Pain Catastrophizing Scale (PCS), comprising 13 items rated on a 5-point scale across the domains of rumination, magnification, and helplessness. Total scores range from 0 to 52, with higher scores reflecting greater catastrophizing. The PCS shows adequate reliability (Cronbach's alpha 0.71–0.93) in people with musculoskeletal pain ( 32 ). 2.8 Sample size calculation The sample size was calculated a priori using G*Power 3.1, based on a fixed-effect linear regression model with an α = 0.05, power (1- ß) = 0.80, and 5 factors. To detect a large effect size ( f 2 = 0.45 ( 33 )) and to account for potential drop-out, 37 participants were needed. 2.9 Statistical Analysis Descriptive demographic and clinical characteristics were summarized using means and standard deviations (SD) or medians and interquartile ranges (IQRs) for continuous variables, depending on the normality of their distribution. Absolute numbers and percentages were used for categorical variables. Normality was assessed using visual, quantitative, and statistical methods ( 34 ). PPTs and headache intensity were analysed using linear mixed-effects regression models (LMMs), fitted by maximum likelihood (ML) estimation. Results are presented as unstandardized beta coefficients with their corresponding 95% confidence intervals (95% CI) and p-values. PPTs (kPa) and headache intensity (NPRS) were considered as the outcome variables. One overall PPT score was derived by averaging three repeated measurements at the trapezius and tibialis anterior muscles. Fixed effects included condition (CPT, MPT, and sham), time (pre vs. post), and period ( 1 , 2 , and 3 ), with a random intercept included per participant to account for between-subject variability. An autoregressive covariance structure (AR1) was assumed for the repeated measures, given that only two time points were available per participant. This structure is considered equivalent to assuming compound symmetry ( 35 , 36 ). Estimated marginal means (EMMs) were calculated, and post-hoc pairwise comparisons were adjusted using Tukey’s Honestly Significant Difference (HSD) method ( 37 , 38 ). The assumption of homogeneity of variances for Tukey’s HSD was evaluated through visual inspection of residual plots and Levene’s test results. Potential order effects were controlled by including period as a fixed effect in all statistical models ( 35 , 39 ). Model selection and the inclusion of random slopes were based on comparisons of likelihood ratio tests (LRT), Akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC) ( 40 ). Before model fitting, assumptions of linear regression (linearity, normality of residuals, and homoscedasticity) were assessed. To assess the potential mechanistic link between CPM effects and change in headache, two correlational approaches were used. Pearson correlation ( r ) was used to calculate the linear relationship between the continuous change in headache intensity and the continuous change in combined mean PPTs (post-condition minus pre-condition), performed separately for each condition group (MPT, CPT, and sham). Point-Biserial correlation ( r pb ) was used to assess the relationship between the change in headache intensity and the CPM-responder status. Participants were classified as responders if their mean combined PPTs increased by > 10% following the CPT. This threshold was anchored to the standard error of measurement (SEM) to ensure changes exceeded measurement noise ( 41 , 42 ). The primary assumption for the Pearson correlation, including linearity, was assessed through visual inspection of scatter plots. The assumption of approximate normality of the continuous variable within each group for the Point-Biserial correlation was also visually verified. Results for both correlations are shown with 95% CIs and p-values. Exploratory analyses for effect modification were conducted by including a three-way interaction term (condition, time, and candidate variable) into the mixed-effects models. Potential effect modifiers, including age, sex, headache-related disability, pain catastrophizing, and analgesic medication use, were assessed for their hypothesized influence on CPM as indicated by previous research ( 16 , 19 , 43 – 45 ). An effect modifier was considered relevant if the three-way interaction term was statistically significant (p < 0.05) or if its inclusion changed the estimated treatment effect by ≥ 10% ( 46 ). The statistical significance level for all analyses was set at p < 0.05. All analyses were conducted using R (version 2024.04.02 + 764; R Foundation for Statistical Computing, Vienna, Austria). LMMs were implemented using the nlme package; post-hoc comparisons with Tukey’s HSD adjustments were conducted using the emmeans package. All data visualizations were generated using the ggplot2 package. 3 Results 3.1 Descriptives Thirty-seven participants were included and completed the required pre- and post-condition assessments across the three study periods. The mean (SD) age was 46 ( 11 ) years, and 70% was female. Detailed baseline characteristics are presented in Table 1 . Table 1 Baseline characteristics of the study sample Baseline characteristics (n = 37) Age (mean ± SD) 46.6 ± 11.3 Sex Male, n (%) 11 (29.7%) Female, n (%) 26 (70.3%) Headache Intensity Score-6 (36–78) (mean ± SD) 63.70 ± 3.38 Pain Catastrophizing Scale (0–52) (mean ± SD) 19.30 ± 9.22 Analgesic medication use Yes, n (%) 28 (75.7%) No, n (%) 9 (24.3%) Headache history (years) (median (IQR)) 10 ( 3 – 25 ) Headache frequency (d/m) (mean ± SD) 18.16 ± 7.50 3.2 Outcomes 3.2.1 PPTs on the trapezius muscle per condition group Application of MPTs resulted in significantly higher PPTs compared to sham MPT (β = 40.64, 95% CI [22.06, 59.23], p < 0.001) and differed significantly compared to CPT (β = 7.2, 95% CI [-11.28, 25.68], p = 0.45), indicating comparable effects of both conditions on PPTs. EMMs after conditions were 280.57 kPa (95% CI [254.05, 307.88]) for MPT, 282.62 kPa (95% CI [256.09, 309.15]) for CPT, and 254.58 kPa (95% CI [228.08, 281.08]) for sham MPT. Post-hoc comparisons confirmed that both MPT and CPT yielded significantly higher PPTs than sham (p < 0.003). No statistically significant difference was found between MPT and CPT (p = 0.96). Figure 1 illustrates the distribution of PPT values per condition, both before and after the conditions. Figure 2 visualizes the EMMs with their 95% CIs derived from the fitted model, illustrating pre- and post-condition changes for each condition. Raincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test. PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test. 3.2.2 PPTs on the tibialis anterior muscle per condition group Application of MPTs resulted in significantly higher PPTs compared to sham MPT (MPT: β = 25.31, 95% CI [5.00, 45.61], p = 0.017). No statistically significant difference was found between MPTs and CPT (β = 11.39, 95% CI [-8.81, 31.58], p = 0.28), indicating comparable effects of both conditions on PPTs. EMMs after conditions were 293.27 kPa (95% CI [262.66, 323.87]) for MPT, 296.38 kPa (95% CI [265.76, 327.00]) for CPT, and 271.88 kPa (95% CI [241.29, 302.47]) for sham MPT. Post-hoc comparisons confirmed that both MPTs and CPT yielded significantly higher PPTs than sham (p < 0.04). No statistically significant difference was found between MPT and CPT (p = 0.93). Figure 3 illustrates the distribution of PPT values per condition, both before and after conditions. Figure 4 visualizes the EMMs with their 95% CIs derived from the fitted model, illustrating pre- and post-condition changes. Raincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test. PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test. 3.2.3 PPTs on the trapezius and tibialis anterior muscles combined per condition group MPTs resulted in significantly higher PPTs compared to sham MPT (β = 32.50, 95% CI [17.05, 47.95], p = 0.0001), while no statistical difference was found between the MPT and CPT application (β = 2.12, 95% CI [-13.24, 17.49], p = 0.79), indicating comparable effects of both conditions on PPTs. EMMs after conditions were 287.54 kPa (95% CI [263.18, 311.89]) for MPT, 289.43 kPa (95% CI [265.06, 313.79]) for CPT, and 263.68 kPa (95% CI [239.34, 288.01]) for sham MPT. Post-hoc comparisons confirmed that both the application MPTs and CPT yielded significantly higher PPTs than sham (p < 0.002). No statistically significant difference was found between MPT and CPT (p = 0.96). Figure 5 illustrates the distribution of PPT values per condition, both before and after conditions. Figure 6 visualizes the EMMs with their 95% CIs derived from the fitted model, illustrating pre-and post-condition changes. Raincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test. PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test. 3.2.4 Headache intensity MPTs resulted in a significantly greater reduction in headache intensity compared to both CPT (β = 1.42, 95% CI [0.78, 2.07]) and sham (β = 1.74, 95% CI [1.10, 2.39]), with p-values < 0.0001. Post-condition EMMs were 2.00 (95% CI [1.08, 2.91]) for MPT, 2.94 (95% CI [2.02, 3.86]) for CPT, and 3.06 (95% CI [2.14, 3.98]) for sham. Pairwise differences showed no significant difference between CPT and sham (p = 0.91), while the statistical difference between MPT and sham and between MPT and CPT was confirmed (p < 0.003). Figure 7 shows the distribution of NPRS scores before and after conditions, while Fig. 8 presents the EMMs with their 95% CIs derived from the fitted model. Raincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. MPT = manual pressure technique; CPT = cold pressor test. MPT = manual pressure technique; CPT = cold pressor test; NPRS = numeric pain rating scale. 3.2.5 Correlations between change in headache intensity and change in PPTs No statistically significant correlation was found between change in headache intensity and change in combined mean PPTs of the trapezius and tibialis in any of the groups (MPT: r = -0.05 (95% CI [-0.368, 0.278]), p = 0.767; sham r = -0.179 (95% CI [-0.476, 0.154]), p = 0.289; CPT: r = 0.089 (95% CI [-0.242, 0.402)], p = 0.599). The absence of significant correlations in each condition group is shown in Fig. 9 , which plots change scores for mean PPTs and headache; a negative correlation indicates an increase in PPTs while the NPRS for headache decreases. PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test. 3.2.6 Correlations between conditioned pain modulation responder status and change in headache intensity Following MPT, CPM responders demonstrated a significantly greater increase in PPTs compared to non-responders (β = 19.94, 95% CI [2.36, 37.52], p = 0.034). Post-condition EMMs were 295.16 kPa (95% CI [250.76, 339.56]) for non-responders and 281.10 kPa (95% CI [248.79, 313.40]) for responders. Post-hoc difference-in-difference analyses showed a statistical difference between responder and non-responder PPTs (estimate = -19.9, p = 0.039), in favor of the responder group. Figure 10 shows the distribution of PPTs per responder status in the MPT group. The additional correlational analysis between CPM-responder status and change in headache intensity showed a statistically significant negative correlation in the MPT group ( r pb = -0.325, 95% CI [-0.587, -0.001], p = 0.049). Figure 11 shows the distribution of change scores in NPRS in the MPT group, stratified by responder and non-responder status (defined as > 10% change in PPTs after CPT), where a negative value indicates a decrease in headache intensity while PPTs increase. PPT = pressure pain threshold; kPa = kilopascal. Raincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT = pressure pain threshold; kPa = kilopascal. Negative values indicate a decrease in headache while the PPT increases. Raincloud plots display individual data points, density distributions, and box plots. Black diamonds indicate group means. CPM = conditioned pain modulation. 3.2.7 Effect modification Effect modification was assessed for several baseline covariates. As shown in Table 2 , multiple variables met the criteria for relevance (statistical significance and/or a change in effect estimate > 10%), primarily driven by a change in effect estimate > 10%, even when formal statistical significance for the three-way interaction was not always reached. For PPT, medication use showed both statistical significance and clinical relevance (p = 0.003; 31.1%), which indicates that the condition effect was 31.3% higher in participants not using medication compared to those using analgesics. Sex also met the relevance threshold, with male participants showing a 12.5% larger improvement in PPT than females (p = 0.116). Regarding headache intensity, the clinical relevance was most pronounced for sex (55.4%) and medication use (18.6%). Specifically, the reduction in NPRS scores was 55.4% greater in men and 18.6% larger in the non-medication group, although these interactions did not reach formal statistical significance. For CPM status, medication use (11.9%) and headache-related disability (20.7%) were identified as relevant modifiers. The effect of CPM status on the outcome was 11.9% stronger in participants without medication and 20.7% more pronounced in those with higher headache-related disability. In contrast, age and pain catastrophizing did not substantially modify the condition effects across any of the outcomes. Table 2 Effect modification on outcomes and candidate variables. Outcome Medication use Sex PCS Age HIT-6 PPT p = 0.003; 31.1%* p = 0.116; 12.5% p = 0.075; 0.4% p = 0.623; 2,2% p = 0.182; 1.2% Headache intensity p = 0.156; 18.6% p = 0.768; 55.4% p = 0.330; 1.9% p = 0.169; 3.6% p = 0.320; 0,4% CPM p = 0.532; 11.9% p = 0.367; 8.2% p = 0.848; 7.4% p = 0.607; 2.0% p = 0.678; 20.7% *Indicates statistical significance at p < 0.05. Percentages represent the percentage change of the effect estimate (derived from model comparison). PPT = pressure pain thresholds; CPM = conditioned pain modulation; PCS = pain catastrophizing scale; HIT-6 = headache impact test-6. 4 Discussion 4.1 Main findings This randomized crossover trial demonstrated that MPTs significantly increased PPTs at local and distal sites, as well as the overall average, compared to sham, with an effect comparable to the CPT. Notably, while both MPT and CPT enhanced PPTs, only MPT produced a statistically significant reduction in headache intensity compared to sham and CPT. Furthermore, while headache reduction was not linearly correlated with the magnitude of PPT change, a significant linear association was found through the responder analysis. Participants classified as CPM responders demonstrated a significantly greater reduction in headache intensity following MPT than non-responders, along with a more pronounced increase in post-condition PPTs. Finally, exploratory effect modification analyses revealed that the absence of analgesic medication use, and male sex were the most consistent effect modifiers across outcomes. In contrast, age and pain catastrophizing did not substantially modify the condition effects across any of the outcomes. These findings suggest that while MPTs and CPT may activate descending pain modulatory pathways, their clinical impact differs. The comparable increase in PPTs across both conditions confirms that MPTs effectively engage pain modulatory pathways. These findings align with observations in healthy cohorts ( 16 , 17 ), mirroring both the direction and magnitude of those effects, and suggest that similar modulatory effects also occur in patients with CTTH. However, the lack of headache reduction following CPT suggests that headache reduction is not merely a function of CPM. This is further supported by the absence of a linear correlation between PPTs and headache intensity, although this likely reflects a restricted range, as participants reported minimal headache changes despite PPT variability ( 47 , 48 ). Instead, the effect of MPT on headache might suggest a possible additional recruitment of localized neuromodulatory circuits within the trigeminal cervical complex (TCC), as discussed in previous studies ( 4 , 7 , 15 ). The significant role of CPM-responder status further elucidates this mechanism. The findings that responders derive more benefit from MPT indicate that these techniques may amplify existing adequate descending inhibition. Mechanistically, MPTs activate Aδ- and C-fiber afferents that trigger descending inhibition via the ventrolateral periaqueductal grey (vlPAG). While the CPT typically triggers a generalized, stress-induced analgesic response, MPTs may recruit more specific inhibitory pathways via the vlPAG and the rostral ventromedial medulla (RVM). These structures serve as key hubs for the descending control of nociception and are effectively engaged by manual interventions ( 49 ). The effectiveness of MPTs in this study could possibly be explained by the topographical nature of such modulation, exerting segment-specific effects within the TCC ( 50 – 52 ). This may be particularly relevant in chronic pain states such as CTTH, where central sensitization is characterized by facilitated synaptic plasticity (e.g., long-term potentiation in the dorsal horn C1-3). This increase in synaptic strength facilitates pain transmission even with normal afferent input ( 53 , 54 ). Consequently, MPTs, acting as a localized mechanical stimulus, may be uniquely effective at tuning these sensitized synapses within the TCC. This spatial specificity explains why MPTs outperformed the CPT's distal nociceptive input in reducing headache intensity. These findings align with prior research indicating that distal stimuli primarily modulate distal pain, whereas proximal stimuli are more effective in proximal regions ( 17 , 27 , 55 ). Furthermore, the observation that effect modifiers influenced outcomes variably suggests that MPT responsiveness is driven by the patient’s dynamic physiological state rather than isolated, static traits. This aligns with the inconsistent findings in existing literature suggesting that a higher cognitive-affective burden and polypharmacy may blunt responsiveness through impaired descending inhibition or altered central sensitization ( 19 , 43 , 45 ). Such variability further supports the neurophysiological framework by Hoegh and Bannister ( 56 ), which posits that the descending pain modulatory system is a highly plastic network sensitive to immediate contextual and physiological fluctuations. Consequently, static trait-level questionnaires may be insufficient to capture the momentary modulatory capacity required to assess effect modification ( 16 , 57 , 58 ). This is reflected in the current findings, where psychological traits such as pain catastrophizing failed to meaningfully modify the treatment effect. In contrast, medication use and sex, representing more direct physiological factors, emerged as more robust modifiers. It is therefore possible that real-time, state-dependent assessments would yield different or more consistent moderation effects than the baseline trait measurements used in the current study. 4.2 Strengths and limitations Our study contains some limitations. First, the sample size may have been underpowered to detect complex moderation interactions involving continuous variables and multi-level model structures, such as autoregressive structures and random effects. This increases the risk of unstable estimates and inflation of error rates, particularly when changes are numerically small but proportionally large ( 59 – 61 ). Furthermore, while the use of a threshold to define CPM-responder is essential to distinguish true physiological change from measurement noise, the application of a fixed 10% cut-off represents a potential limitation. Fixed percentage-based thresholds are common in clinical research ( 62 , 63 ), yet they remain somewhat arbitrary and may lead to misclassification if they do not account for study-specific variability ( 42 , 64 , 65 ). To mitigate this, we anchored our 10% cut-off to the SEM derived from our baseline PPT measurements. By using a threshold supported by absolute reliability metrics rather than a purely arbitrary percentage, we minimized the risk of overestimating the CPM effect due to natural variability and enhanced our ability to distinguish true physiological modulation from measurement error ( 41 , 66 , 67 ). A strength of this study is its rigorous randomized crossover design, conducted in accordance with CONSORT guidelines. By utilizing participants as their own controls and incorporating a washout period, the design minimized between-subject variability, (temporal) confounding, and potential carry-over effects. The internal validity was further reinforced by multi-level blinding, encompassing participants, outcome assessors, and data analysts. While the nature of the intervention precluded clinician blinding, the separation of research roles and the inclusion of a sham condition mitigated expectancy effects and controlled for the non-specific effects of manual contact. This methodological rigor extended to the analytical framework; we employed LMMs with autoregressive structures and random intercepts, while controlling for period effects, to account for repeated measures and temporal dependencies inherent in the crossover design. The use of EMMs and visualizations subsequently allowed for the interpretation of treatment-by-time interactions. Furthermore, the application of a standardized and validated CPM protocol ( 29 ) ensured high construct validity, aligning our findings with current gold standards in experimental pain research. 4.3 Implications The findings of this study suggest that MPTs may offer clinically meaningful benefits for individuals with CTTH, demonstrating that manual interventions remain effective even in people with CTTH, where pain modulation often is impaired. Notably, the comparable increases in PPTs following both MPT and CPT suggest that manual techniques are as effective in engaging descending pain-modulatory pathways. This underscores the possible therapeutic value of MPTs for activating endogenous pain control, despite the neuroplastic changes associated with chronic pain states. Furthermore, screening for CPM-responder status in clinical practice and research may help identify individuals who are most likely to respond to MPT treatment, serving as a pragmatic tool to guide personalized treatment planning and increase effect sizes. 4.4 Recommendations Based on the findings and limitations of the current study, several possibilities for future research are proposed to advance the clinical application of MPTs in people with CTTH. First, while this study provides preliminary insights into treatment response, the sample size may have constrained the detection of subtle moderator effects within the complex multi-level model structure. Future research should employ larger cohorts to validate candidate effect modifiers, prioritizing state-based physiological assessments over conventional trait-level questionnaires. Second, there is a need for methodological standardization. Future studies should focus on determining population-specific reliability metrics, such as the SEM and SDC, for defining responder criteria in CTTH that distinguish true neurophysiological changes from inherent measurement noise. Establishing these thresholds will facilitate the development of CPM-responder profiles, supporting a transition toward more personalized and mechanism-informed physiotherapy strategies. Finally, future research should investigate the dose-response relationships of MPTs by varying treatment, such as dosage, intensity, and frequency of MPTs. Incorporating extended follow-up periods will help to evaluate the longitudinal durability of these neuromodulatory effects and to better understand the temporal dynamics of neuroplastic adaptation in CTTH. 5 Conclusion This study demonstrates that MPTs effectively engage descending pain modulatory pathways in individuals with CTTH, yielding physiological effects comparable to the CPT. However, while both conditions enhanced PPTs, only MPTs led to a clinically significant reduction in headache intensity. This improvement was not linearly correlated with the magnitude of PPT change but was instead significantly associated with individual CPM-responder status. While certain baseline factors modified effect estimates, the variability across outcomes suggests that results are primarily driven by the patient’s dynamic physiological state. Overall, the results seem to support the clinical utility of MPTs in managing CTTH and provide mechanistic insights that may inform future research and the development of personalized, mechanism-informed physiotherapy strategies. Abbreviations CTTH: Chronic tension-type headache; MPT: Manual pressure technique; CPT: Cold pressor test; PPT: Pressure pain threshold; NPRS: Numeric pain rating scale; CPM: Conditioned pain modulation; LMM: Linear mixed-effects model; CI: Confidence interval; kPa: kilopascal; EMM: estimated marginal mean. Declarations Acknowledgements The authors would like to thank the MSG Science Netwerk Fysiotherapie for their support of this project and all participants for their time and contribution to this study. 6.1 Ethics approval and consent to participate This study protocol was approved by the Ethics Committee of the Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam (reference number: VCWE-2025-015). Written informed consent was obtained from all participants. 6.2 Consent for publication Not applicable. 6.3 Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon request. 6.4 Competing interests The authors declare that they have no competing interests. 6.5 Funding No funding was received for this study. 6.6 Authors’ contributions BtM: study concept and design, acquisition of data, statistical analysis and interpretation of data, and preparation of the manuscript. RC: study concept and design, acquisition of data, statistical analysis and interpretation of data, and preparation of the manuscript. XZ: study concept and design, acquisition of data, and preparation of the manuscript. BvdM: study concept and design, preparation of the manuscript. GSP: study concept and design, preparation of the manuscript. All authors have read and approved the final manuscript. References Yu S, Han X (2015) Update of Chronic Tension-Type Headache. Curr Pain Headache Rep January 19(1):469. 10.1007/s11916-014-0469-5 Ashina S, Buse DC, Bjorner JB, Bendtsen L, Lyngberg AC, Jensen RH (2021) e.a. Health-related quality of life in tension-type headache: a population-based study. Scandinavian Journal of Pain. 26 October ;21(4):778 – 87. 10.1515/sjpain-2020-0166 Pan LLH, Ling YH, Wang SJ, Al-Hassany L, Chen WT, Chiang CC (2025) e.a. Hallmarks of primary headache: part 2– Tension-type headache. J Headache Pain. 17 July ;26(1):164. 10.1186/s10194-025-02098-w Castien R, De Hertogh W (2019) A Neuroscience Perspective of Physical Treatment of Headache and Neck Pain. Front Neurol 26 march 10:276. 10.3389/fneur.2019.00276 De Hertogh W, Amons A, Van Daele L, Vanbaelen E, Castien R (October 2022) Modulation of Central Nociceptive Transmission by Manual Pressure Techniques in Patients with Migraine: An Observational Study. JCM 25(21):6273. 10.3390/jcm11216273 Do TP, Heldarskard GF, Kolding LT, Hvedstrup J, Schytz HW (2018) Myofascial trigger points in migraine and tension-type headache. J Headache Pain Dec 19(1):84. 10.1186/s10194-018-0913-8 Watson DH, Drummond PD (2012) Head Pain Referral During Examination of the Neck in Migraine and Tension-Type Headache. Headache September 52(8):1226–1235. 10.1111/j.1526-4610.2012.02169.x Ashina M (2004) Neurobiology of Chronic Tension-Type Headache. Cephalalgia March 24(3):161–172. 10.1111/j.1468-2982.2003.00644.x Castien R, Duineveld D, De Hertogh W, Scholten-Peeters G (2021) Pericranial Total Tenderness Score in Patients with Tension-type Headache and Migraine: A Systematic Review and Meta- analysis. Pain Physician. ;24(8) Pensri C, Liang Z, Treleaven J, Jull G, Thomas L (2025) Cervical musculoskeletal impairments in migraine and tension-type headache and relationship to pain related factors: An updated systematic review and meta-analysis. Musculoskelet Sci Pract April 76:103251. 10.1016/j.msksp.2024.103251 Herranz-Gómez A, García-Pascual I, Montero-Iniesta P, Touche RL, Paris-Alemany A (2021) Effectiveness of Exercise and Manual Therapy as Treatment for Patients with Migraine, Tension-Type Headache or Cervicogenic Headache: An Umbrella and Mapping Review with Meta-Meta-Analysis. Appl Sci 26 July 11(15):6856. 10.3390/app11156856 Jung A, Eschke RC, Struss J, Taucher W, Luedtke K (2022) Effectiveness of physiotherapy interventions on headache intensity, frequency, duration and quality of life of patients with tension-type headache. A systematic review and network meta-analysis. Cephalalgia August 42(9):944–965. 10.1177/03331024221082073 Krøll LS, Callesen HE, Carlsen LN, Birkefoss K, Beier D, Christensen HW (2021) e.a. Manual joint mobilisation techniques, supervised physical activity, psychological treatment, acupuncture and patient education for patients with tension-type headache. A systematic review and meta-analysis. J Headache Pain. December ;22(1):96. 10.1186/s10194-021-01298-4 Jiang W, Li Z, Wei N, Chang W, Chen W, Sui HJ (2019) Effectiveness of physical therapy on the suboccipital area of patients with tension-type headache: A meta-analysis of randomized controlled trials. Med May 98(19):e15487. 10.1097/MD.0000000000015487 Lascurain-Aguirrebeña I, Newham D, Critchley DJ (2016) Mechanism of Action of Spinal Mobilizations: A Systematic Review. SPINE January 41(2):159–172. 10.1097/BRS.0000000000001151 Arribas-Romano A, Fernández-Carnero J, Rodríguez-Lagos L, Molina-Álvarez M, Zabala-Zambrano J, Lezaun-Hernández L (2024) e.a. CPM-Related Mechanisms Could Play a Key Role in the Effects on Pain Sensitivity Induced by Manual Therapy: Three Crossover Trials Investigating the Effects of Manual Pressure. JCM. 22 June ;13(13):3648. 10.3390/jcm13133648 Castien R, Reezigt RR, Den Hartog R, Amons A, De Hertogh W, Scholten-Peeters GG (April 2025) Conditioned pain modulation elicited through manual pressure techniques on the cervical spine: a crossover study. PR9. 10(2):e1258. 10.1097/PR9.0000000000001258 Nir RR, Yarnitsky D (June 2015) Conditioned pain modulation. Current Opinion in Supportive & Palliative Care. 9(2):131–137. 10.1097/SPC.0000000000000126 Ramaswamy S, Wodehouse T (2021) Conditioned pain modulation—A comprehensive review. Neurophysiologie Clinique June 51(3):197–208. 10.1016/j.neucli.2020.11.002 Williams AE, Miller MM, Bartley EJ, McCabe KM, Kerr KL, Rhudy JL (2019) Impairment of Inhibition of Trigeminal Nociception via Conditioned Pain Modulation in Persons with Migraine Headaches. Pain Med 1 August 20(8):1600–1610. 10.1093/pm/pny305 Staud R (2013) The important role of CNS facilitation and inhibition for chronic pain. Int J Clin Rheumatol Dec 8(6):639–646. 10.2217/ijr.13.57 Exposto FG, Bendixen KH, Ernberg M, Bach FW, Svensson P (2021) Assessment of Pain Modulatory and Somatosensory Profiles in Chronic Tension-Type Headache Patients. Pain Med 8 Oct 22(10):2356–2365. 10.1093/pm/pnab084 Lewis GN, Rice DA, McNair PJ (2012) Conditioned Pain Modulation in Populations With Chronic Pain: A Systematic Review and Meta-Analysis. J Pain Oct 13(10):936–944. 10.1016/j.jpain.2012.07.005 Geri T, Viceconti A, Minacci M, Testa M, Rossettini G (2019) Manual therapy: Exploiting the role of human touch. Musculoskelet Sci Pract Dec 44:102044. 10.1016/j.msksp.2019.07.008 Dwan K, Li T, Altman DG, Elbourne D CONSORT 2010 statement: extension to randomised crossover trials. BMJ. 31 July 2019;l4378. 10.1136/bmj.l4378 Biurrun Manresa JA, Fritsche R, Vuilleumier PH, Oehler C, Mørch CD, Arendt-Nielsen L (2014) e.a. Is the Conditioned Pain Modulation Paradigm Reliable? A Test-Retest Assessment Using the Nociceptive Withdrawal Reflex. Zuo XN. PLoS ONE. 20 June ;9(6):e100241. 10.1371/journal.pone.0100241 Reezigt RR, Kielstra SC, Coppieters MW, Scholten-Peeters GGM (2021) No relevant differences in conditioned pain modulation effects between parallel and sequential test design. A cross-sectional observational study. PeerJ 14 Dec 9:e12330. 10.7717/peerj.12330 Ibancos-Losada MDR, Osuna-Pérez MC, Castellote-Caballero MY, Díaz-Fernández Á (2020) Conditioned Pain Modulation Effectiveness: An Experimental Study Comparing Test Paradigms and Analyzing Potential Predictors in a Healthy Population. Brain Sci 30 August 10(9):599. 10.3390/brainsci10090599 Lewis GN, Luke H, Rice DA, Rome K, McNair PJ (2012) Reliability of the Conditioned Pain Modulation Paradigm to Assess Endogenous Inhibitory Pain Pathways. Pain Res Manage January 17(2):98–102. 10.1155/2012/610561 Young IA, Dunning J, Butts R, Cleland JA, Fernández-de-las-Peñas C (2019) Psychometric properties of the Numeric Pain Rating Scale and Neck Disability Index in patients with cervicogenic headache. Cephalalgia January 39(1):44–51. 10.1177/0333102418772584 Martin M, Blaisdell B, Kwong JW, Bjorner JB (2004) The Short-Form Headache Impact Test (HIT-6) was psychometrically equivalent in nine languages. J Clin Epidemiol Dec 57(12):1271–1278. 10.1016/j.jclinepi.2004.05.004 Severeijns R, Van Den Hout MA, Vlaeyen JWS, Picavet SJH (2002) Pain catastrophizing and general health status in a large Dutch community sample. Pain September 99(1):367–376. 10.1016/S0304-3959(02)00219-1 Zieliński G (2025) Effect Size Guidelines for Individual and Group Differences in Physiotherapy. Archives Phys Med Rehabilitation Dec 106(12):1844–1849. 10.1016/j.apmr.2025.05.013 Altman DG, Bland JM (1995) Statistics notes: The normal distribution. BMJ 4 Febr 310(6975):298–298. 10.1136/bmj.310.6975.298 Fitzmaurice GM, Laird NM, Ware JH (2011) Applied longitudinal analysis. 2nd ed. Hoboken, N.J: Wiley; 701 p. (Wiley series in probability and statistics) Twisk JWR (2013) Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide [Internet]. 2de ed. Cambridge University Press; 10.1017/CBO9781139342834 Field A, Miles J, Field Z (2014) Discovering statistics using R. Repr. Sage, Los Angeles, CA, USA, p 957 Maxwell SE, Delaney HD, Kelley K (2017) Designing Experiments and Analyzing Data: A Model Comparison Perspective [Internet]. 3de dr. Third edition / Scott E. Maxwell, Harold D. Delaney, and Ken Kelley. New York,: Routledge; 10.4324/9781315642956 Jones B, Kenward MG (2003) Design and Analysis of Cross-Over Trials [Internet]. ed. Chapman and Hall/CRC, p 0. 10.1201/9781420036091 Burnham KP, Anderson DR, Burnham KP (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New York, p 488 De Vet HCW, Terwee CB, Knol DL, Bouter LM (2006) When to use agreement versus reliability measures. J Clin Epidemiol Oct 59(10):1033–1039. 10.1016/j.jclinepi.2005.10.015 Wyrwich KW (2004) Minimal Important Difference Thresholds and the Standard Error of Measurement: Is There a Connection? J Biopharm Stat 24 Dec 14(1):97–110. 10.1081/BIP-120028508 Hermans L, Van Oosterwijck J, Goubert D, Goudman L, Crombez G, Calders P (2016) e.a. Inventory of Personal Factors Influencing Conditioned Pain Modulation in Healthy People: A Systematic Literature Review. Pain Practice. July ;16(6):758 – 69. 10.1111/papr.12305 Mertens MG, Hermans L, Crombez G, Goudman L, Calders P, Van Oosterwijck J (2021) e.a. Comparison of five conditioned pain modulation paradigms and influencing personal factors in healthy adults. European Journal of Pain. January ;25(1):243 – 56. 10.1002/ejp.1665 Nahman-Averbuch H, Nir RR, Sprecher E, Yarnitsky D (2016) Psychological Factors and Conditioned Pain Modulation: A Meta-Analysis. Clin J Pain June 32(6):541–554. 10.1097/AJP.0000000000000296 Sun X, Briel M, Walter SD, Guyatt GH (2010) Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses. BMJ 30 March 340(mar30 3):c117–c117. 10.1136/bmj.c117 Bland JM, Altman DG (2011) Correlation in restricted ranges of data. BMJ 11 March 342(mar11 1):d556–d556. 10.1136/bmj.d556 Goodwin LD, Leech NL (2006) Understanding Correlation: Factors That Affect the Size of r. J Experimental Educ April 74(3):249–266. 10.3200/JEXE.74.3.249-266 Hoegh M, Bannister K (2024) Pain Science in Practice (Part 6): How Does Descending Modulation of Pain Work? . J Orthop Sports Phys Therapy Febr 54(2):97–100. 10.2519/jospt.2024.12112 Linnman C, Moulton EA, Barmettler G, Becerra L, Borsook D (2012) Neuroimaging of the periaqueductal gray: State of the field. NeuroImage March 60(1):505–522. 10.1016/j.neuroimage.2011.11.095 Mehnert J, Tinnermann A, Basedau H, May A (2024) Functional representation of trigeminal nociceptive input in the human periaqueductal gray. Sci Adv 22 March 10(12):eadj8213. 10.1126/sciadv.adj8213 Tinoco Mendoza FA, Hughes TES, Robertson RV, Crawford LS, Meylakh N, Macey PM (2023) e.a. Detailed organisation of the human midbrain periaqueductal grey revealed using ultra-high field magnetic resonance imaging. NeuroImage. February ;266:119828. 10.1016/j.neuroimage.2022.119828 Hoegh M (2023) Pain Science in Practice (Part 5): Central Sensitization II . J Orthop Sports Phys Therapy Febr 53(2):55–58. 10.2519/jospt.2023.11571 Hoegh M (2023) Pain Science in Practice (Part 4): Central Sensitization I . J Orthop Sports Phys Therapy January 53(1):1–4. 10.2519/jospt.2023.11569 Granovsky Y, Sprecher E, Yarovinsky N, Shor M, Crystal S (2024) Body-site effect on CPM efficiency in healthy subjects: Central vs. peripheral stimulation. Heliyon Febr 10(3):e25156. 10.1016/j.heliyon.2024.e25156 Hoegh M, Bannister K (2024) Pain Science in Practice (Part 7): How Is Descending Modulation of Pain Measured? . J Orthop Sports Phys Therapy March 54(3):155–160. 10.2519/jospt.2024.12113 Millan MJ (2002) Descending control of pain. Progress Neurobiol April 66(6):355–474. 10.1016/S0301-0082(02)00009-6 Yarnitsky D, Arendt-Nielsen L, Bouhassira D, Edwards RR, Fillingim RB, Granot M (2010) e.a. Recommendations on terminology and practice of psychophysical DNIC testing. European Journal of Pain. April ;14(4):339–339. 10.1016/j.ejpain.2010.02.004 Lee PH (2014) Is a Cutoff of 10% Appropriate for the Change-in-Estimate Criterion of Confounder Identification? J Epidemiol 24(2):161–167. 10.2188/jea.JE20130062 Leon AC, Heo M (2009) Sample sizes required to detect interactions between two binary fixed-effects in a mixed-effects linear regression model. Comput Stat Data Anal January 53(3):603–608. 10.1016/j.csda.2008.06.010 Maas CJM, Hox JJ (2005) Sufficient Sample Sizes for Multilevel Modeling. Methodol January 1(3):86–92. 10.1027/1614-2241.1.3.86 Locke D, Gibson W, Moss P, Munyard K, Mamotte C, Wright A (2014) Analysis of Meaningful Conditioned Pain Modulation Effect in a Pain-Free Adult Population. J Pain November 15(11):1190–1198. 10.1016/j.jpain.2014.09.001 Yarnitsky D, Crispel Y, Eisenberg E, Granovsky Y, Ben-Nun A, Sprecher E (2008) e.a. Prediction of chronic post-operative pain: Pre-operative DNIC testing identifies patients at risk. Pain. 15 August ;138(1):22 – 8. 10.1016/j.pain.2007.10.033 Atkinson G, Nevill AM (1998) Statistical Methods For Assessing Measurement Error (Reliability) in Variables Relevant to Sports Medicine. Sports Med 26(4):217–238. 10.2165/00007256-199826040-00002 Kennedy DL, Kemp HI, Ridout D, Yarnitsky D, Rice ASC (2016) Reliability of conditioned pain modulation: a systematic review. Pain November 157(11):2410–2419. 10.1097/j.pain.0000000000000689 Kennedy DL, Kemp HI, Wu C, Ridout DA, Rice ASC (2020) Determining Real Change in Conditioned Pain Modulation: A Repeated Measures Study in Healthy Volunteers. J Pain May 21(5–6):708–721. 10.1016/j.jpain.2019.09.010 Weir JP (2005) Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Conditioning Res 19(1):231–240 Flowchart 1 Flowchart 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Flowchart1.png Flowchart 1 Study procedure. MPT = manual pressure technique; CPT = cold pressor test; PPT = pressure pain threshold; NPRS = numeric pain rating scale. 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PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/0071bfe08c7672506f810518.png"},{"id":105728288,"identity":"74654416-e982-43dd-9d94-322cfe63c0dc","added_by":"auto","created_at":"2026-03-30 11:11:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":58001,"visible":true,"origin":"","legend":"\u003cp\u003ePre- and post-condition model estimated marginal means with 95% CIs on the tibialis anterior muscle.\u003c/p\u003e\n\u003cp\u003ePPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/b5c78296595a3577b0ea1a9e.png"},{"id":105641341,"identity":"598f6e78-93be-4c2b-964a-fb6a6daabb90","added_by":"auto","created_at":"2026-03-28 16:28:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":179552,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the combined pressure pain thresholds on the trapezius and tibialis anterior muscle pre- and post-condition.\u003c/p\u003e\n\u003cp\u003eRaincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/e99302e8e1d2f0448ce11255.png"},{"id":105728748,"identity":"1461956b-daf3-4928-985d-ddf4c549fa86","added_by":"auto","created_at":"2026-03-30 11:12:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":60604,"visible":true,"origin":"","legend":"\u003cp\u003ePre- and post-condition estimated marginal means (95% CIs) for combined pressure pain thresholds.\u003c/p\u003e\n\u003cp\u003ePPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/e0b05a0e6bb9c2538915f3aa.png"},{"id":105641345,"identity":"0cabf2e2-3177-4b18-be13-9645b0d4172d","added_by":"auto","created_at":"2026-03-28 16:28:33","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":131953,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of NPRS scores on the headache intensity pre- and post-condition.\u003c/p\u003e\n\u003cp\u003eRaincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. MPT = manual pressure technique; CPT = cold pressor test.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/73e0c3dbfc87009cd092cff3.png"},{"id":105728878,"identity":"28846ae2-cdbe-419f-82d9-950a3c4ad902","added_by":"auto","created_at":"2026-03-30 11:12:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":57307,"visible":true,"origin":"","legend":"\u003cp\u003ePre- and post-condition model estimated marginal means with 95% CIs on headache intensity.\u003c/p\u003e\n\u003cp\u003eMPT = manual pressure technique; CPT = cold pressor test; NPRS = numeric pain rating scale.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/980912db40d7f79c8463bf62.png"},{"id":105728361,"identity":"05e00a01-c963-4317-8194-1ce446fd1cf5","added_by":"auto","created_at":"2026-03-30 11:11:34","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":165557,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between changes in headache intensity and pressure pain thresholds, divided per condition group.\u003c/p\u003e\n\u003cp\u003ePPT = pressure pain threshold; kPa = kilopascal; MPT = manual pressure technique; CPT = cold pressor test.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/9d7bd9ac9c24a77d0b3a3c5b.png"},{"id":105728696,"identity":"41ad3f5a-dfd4-4088-9660-1a5b7da803d4","added_by":"auto","created_at":"2026-03-30 11:12:30","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":174026,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of pressure pain thresholds per responder status pre- and post-condition.\u003c/p\u003e\n\u003cp\u003ePPT = pressure pain threshold; kPa = kilopascal. Raincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT = pressure pain threshold; kPa = kilopascal.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/93a05f04bbbc77bddc471b3c.png"},{"id":105641352,"identity":"3658d06d-a57c-4c7d-b02f-7fa1fffe7347","added_by":"auto","created_at":"2026-03-28 16:28:33","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":107910,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of change scores in NPRS per responder status.\u003c/p\u003e\n\u003cp\u003eNegative values indicate a decrease in headache while the PPT increases. Raincloud plots display individual data points, density distributions, and box plots. Black diamonds indicate group means. CPM = conditioned pain modulation.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/3c3c0b9bb397fa63182a9b9d.png"},{"id":105751931,"identity":"f1c3f13f-9c7d-44c1-b82c-4aba45f2664d","added_by":"auto","created_at":"2026-03-30 15:51:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2578374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/b64ae8cd-a07b-4fd1-9623-8b2ddd9c6811.pdf"},{"id":105728762,"identity":"f4494c6d-956a-4bc1-85a9-c8e8ada01e5d","added_by":"auto","created_at":"2026-03-30 11:12:38","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":172832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart 1\u003c/strong\u003e Study procedure.\u003c/p\u003e\n\u003cp\u003eMPT = manual pressure technique; CPT = cold pressor test; PPT = pressure pain threshold; NPRS = numeric pain rating scale.\u003c/p\u003e","description":"","filename":"Flowchart1.png","url":"https://assets-eu.researchsquare.com/files/rs-9222006/v1/61a2b2929e3022b567038a59.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Manual Pressure Techniques Activate Descending Pain-Modulatory Pathways and Reduce Headache Intensity in Chronic Tension-Type Headache: A Randomized Crossover Trial","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTension-type headache (TTH) is the most prevalent type of primary headache. Chronic tension-type headache (CTTH), defined as having more than 15 headache days per month over a period of more than three months, affects between 0.5% and 4.8% of the global population (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and has a substantial impact on quality of life (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pathophysiological mechanisms of CTTH are not fully understood, although current literature suggests central pain mechanisms play a dominant role (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The trigeminal cervical complex (TCC) with connections to the upper cervical spine is considered a critical pathway due to its pivotal role in the development of TTH (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Referred pain from the upper cervical spine to the head can be elicited through these pathways by applying pressure or stretch to cervical structures such as muscles, ligaments, and joints (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), a process that is reinforced by central and peripheral sensitization (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Given the involvement of these pathways and the frequent co-occurrence of neck pain in CTTH, dysfunctions of the upper cervical spine, including muscles and joints, such as pericranial tenderness and heightened sensitivity to pressure pain, are commonly observed in patients with CTTH and may contribute to its development, maintenance, or exacerbation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch shows that manual therapy can reduce headache frequency and intensity, particularly when combined with exercise interventions (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Manual therapy comprises a range of clinician-applied, hands-on techniques used in the assessment and treatment of musculoskeletal disorders. Commonly used hands-on interventions include manual pressure techniques (MPTs), which apply pressure to specific areas of the upper cervical spine to reduce pain (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Although MPTs are frequently used in clinical practice, a clear mechanistic understanding of MPTs remains elusive. A better understanding of these mechanisms is essential for developing more effective and personalized treatment strategies.\u003c/p\u003e \u003cp\u003eSpinal mobilizations have been shown to activate descending pain modulation mechanisms mediated by the central nervous system (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), a process that may also apply to MPTs, as observed in healthy pain-free individuals (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne method to measure descending pain modulation is through conditioned pain modulation (CPM), also known as the pain-inhibits-pain effect, in which a noxious stimulus applied at one body site inhibits pain and nociception at another, contralateral site (\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Descending pain modulation mechanisms are typically evaluated in clinical research using a noxious cold stimulus, such as the cold pressor test (CPT). These mechanisms involve key brainstem structures, such as the periaqueductal grey and the rostral ventral medulla, which regulate nociceptive transmission via endogenous opioid pathways (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Reduced CPM efficiency has been associated with higher pain sensitivity in various chronic pain conditions (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), including chronic headaches (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current findings support the hypothesis that MPTs may influence sensory processing by stimulating descending pain modulation mechanisms. Therefore, we hypothesize that engagement of these descending pain-modulatory mechanisms contributes to headache reduction in individuals with CTTH. To effectively isolate the specific neurophysiological mechanisms of MPTs, it is necessary to distinguish the actual treatment effects from non-specific treatment effects, such as placebo responses and contextual factors (e.g., patient expectations). This distinction requires the inclusion of a sham-control group (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Furthermore, to specifically test the hypothesis that MPTs engage descending pain modulation, we compare their effects with those of a standardized CPM-activating conditioning stimulus, namely the CPT. Consequently, this study aims to investigate 1) the CPM effects of MPTs applied to the upper cervical spine on pressure pain thresholds (PPTs) and 2) to explore their effects on headache intensity in people with CTTH, comparing these outcomes to both sham MPT and CPT.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eThis randomized crossover trial was conducted in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines for randomized crossover trials (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Participants were randomly assigned to one of six possible sequences, comprising three conditions: MPTs, sham MPTs, and CPT. The study protocol was approved by the Ethics Committee of the Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam (VCWE-2025-015).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003eParticipants were recruited from general practitioners and a regional network of physiotherapy practices specializing in headache from February 2025 to July 2025. Eligible participants were screened by a researcher for CTTH according to ICHD-III criteria, were aged 18\u0026ndash;65, and were able to read either Dutch or English. Participants were excluded if they had a diagnosis of secondary headache (e.g., medication overuse headache), used muscle relaxants, had clinically diagnosed depression and/or anxiety disorders, hypersensitivity to cold stimuli, a history of significant cervical trauma or cervical surgery, or were pregnant. To minimize potential effects on CPM, participants were asked to refrain from consuming caffeine and alcohol for 24 hours before the assessments (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). All participants provided written informed consent before inclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Randomization and blinding\u003c/h2\u003e \u003cp\u003eParticipants were randomly assigned to a computerized allocation sequence that included MPT, sham MPT, and CPT. This randomization generated six possible permutations of the three condition groups. After randomization, a researcher instructed the clinicians to apply the three conditions in the specified order. The clinicians were unaware of the participants\u0026rsquo; characteristics. To maintain blinding of outcome assessment, an independent examiner conducted all PPT measurements before and after each condition. This examiner remained consistent across all participant measurements and sessions and was blinded to the condition allocation. Data from all measurements were recorded by a research assistant who was consistently present in the room. Participants were blinded to the allocation of conditions. Clinicians applying the conditions could not be blinded due to the study\u0026rsquo;s nature. Furthermore, the statistician responsible for the data analysis remained blinded to the condition allocation. Data were provided in a coded format to ensure unbiased processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Procedures\u003c/h2\u003e \u003cp\u003eParticipants were assessed in a single session at the same location. The total duration of the conditions and measurements was approximately 60 minutes. The experimental procedure consisted of four phases: familiarization, baseline measurement including the pre-test stimulus (pressure pain thresholds (PPTs)), conditioning stimulus (CPT, MPT, or sham MPT), and post-test stimulus (PPTs). The order of these phases was different between groups. Each condition session was separated by a 20-minute washout period to minimize carry-over effects (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Measurements within the protocol adhered to a sequential CPM design.\u003c/p\u003e \u003cp\u003eAfter randomization, participants received either MPT, sham MPT, or CPT (period 1). After a 20-minute washout period, they proceeded to a second condition (period 2), followed by another 20-minute washout period and a final condition (period 3). The standardized pre- and post-condition assessment included headache intensity measurement (using the Numeric Pain Rating Scale, NPRS) and PPTs. During each washout period, participants completed the questionnaires addressing perceived limitations due to headaches and pain catastrophizing. The overall procedure is illustrated in Flowchart 1.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFlowchart 1\u003c/b\u003e Study procedure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test; PPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; NPRS\u0026thinsp;=\u0026thinsp;numeric pain rating scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Conditioning stimuli\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Cold pressor test\u003c/h2\u003e \u003cp\u003eThe CPT was used to measure the CPM effect. The CPT is the most commonly used and widely accepted CS in CPM measurements (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The CPT procedure involved immersing the participant's non-dominant hand, up to the wrist, in a cold-water bath equipped with a circulating pump, maintained at 10\u0026ndash;12\u0026deg;C. The immersion lasted 60 seconds, during which the research assistant recorded the participant\u0026rsquo;s pain intensity every 20 seconds using the NPRS (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Manual pressure technique\u003c/h2\u003e \u003cp\u003eThe MPTs were performed by experienced, trained musculoskeletal physiotherapists with expertise in headache and cervical spine disorders. The MPT was administered with both thumbs placed on the participant\u0026rsquo;s suboccipital muscles on the non-dominant side, while the participant remained in a prone position with the cervical spine maintained in a neutral position. The assessor gradually increased pressure to achieve a pain score of at least 5\u0026ndash;6 on the NPRS. A timer was started, and the same pressure was maintained for a maximum of 120 seconds. NPRS scores were recorded every 20 seconds by the research assistant, as previously described by de Hertogh et al. in 2022 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Sham manual pressure technique\u003c/h2\u003e \u003cp\u003eThe sham MPT procedure was designed identically to the active MPT condition. However, only minimal pressure was applied to ensure that no pain was provoked. This light pressure was maintained for 120 seconds and repeated three times, following the exact timing and procedure as active MPTs.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Test stimulus\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 Pressure pain thresholds\u003c/h2\u003e \u003cp\u003ePPTs were applied as the test stimulus (TS). PPTs are widely used and considered as a reference standard for assessing CPM effects (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). PPTs were assessed using a calibrated digital pressure algometer (Type II, Somedic Electronics, Solna, Sweden) with a 1 cm\u0026sup2; probe and expressed in kilo-pascals (kPa). Measurements were conducted before and immediately after each condition at two body sites on the participant\u0026rsquo;s dominant side: the midpoint of the trapezius and the anterior tibialis muscle, while the participants remained in a prone position. The pressure was applied perpendicularly at a rate of 50 kPa/s until the feeling of pressure changed into pain. At that point, they were instructed to press the button and stop the measurement. The value displayed on the algometer at that moment was recorded. PPT measurements were performed three times at each site, and the mean score was used in the analysis. A 20-second interval was maintained between measurements to prevent temporal summation or wind-up phenomena (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The Somedic algometer has shown excellent reliability, with intrarater reliability coefficients ranging from 0.90 to 0.95 in healthy participants and from 0.89 to 0.96 in individuals with migraine (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Questionnaires\u003c/h2\u003e \u003cp\u003eThe NPRS was used to assess self-reported headache intensity at the start and end of every period. Participants were asked to rate their current pain level on an 11-point scale, ranging from 0 (no pain) to 10 (the worst pain imaginable). The NPRS has demonstrated good intrarater reliability (r\u0026thinsp;=\u0026thinsp;0.72) in participants with headaches (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Headache Impact Test (HIT-6) measures the impact of headaches across various domains, including work, social life, and cognitive function. Total scores range from 36 to 78, with higher scores indicating greater impact, categorized into four severity levels. The Dutch version has demonstrated psychometric equivalence and good reliability (intrarater reliability coefficient 0.78\u0026ndash;0.90) in people with headache (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePain catastrophizing was assessed using the Pain Catastrophizing Scale (PCS), comprising 13 items rated on a 5-point scale across the domains of rumination, magnification, and helplessness. Total scores range from 0 to 52, with higher scores reflecting greater catastrophizing. The PCS shows adequate reliability (Cronbach's alpha 0.71\u0026ndash;0.93) in people with musculoskeletal pain (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Sample size calculation\u003c/h2\u003e \u003cp\u003eThe sample size was calculated a priori using G*Power 3.1, based on a fixed-effect linear regression model with an α\u0026thinsp;=\u0026thinsp;0.05, power (1-\u003cem\u003e\u0026szlig;)\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80, and 5 factors. To detect a large effect size (\u003cem\u003ef\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.45 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)) and to account for potential drop-out, 37 participants were needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive demographic and clinical characteristics were summarized using means and standard deviations (SD) or medians and interquartile ranges (IQRs) for continuous variables, depending on the normality of their distribution. Absolute numbers and percentages were used for categorical variables. Normality was assessed using visual, quantitative, and statistical methods (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePPTs and headache intensity were analysed using linear mixed-effects regression models (LMMs), fitted by maximum likelihood (ML) estimation. Results are presented as unstandardized beta coefficients with their corresponding 95% confidence intervals (95% CI) and p-values. PPTs (kPa) and headache intensity (NPRS) were considered as the outcome variables. One overall PPT score was derived by averaging three repeated measurements at the trapezius and tibialis anterior muscles. Fixed effects included condition (CPT, MPT, and sham), time (pre vs. post), and period (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, and \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), with a random intercept included per participant to account for between-subject variability. An autoregressive covariance structure (AR1) was assumed for the repeated measures, given that only two time points were available per participant. This structure is considered equivalent to assuming compound symmetry (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Estimated marginal means (EMMs) were calculated, and post-hoc pairwise comparisons were adjusted using Tukey\u0026rsquo;s Honestly Significant Difference (HSD) method (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The assumption of homogeneity of variances for Tukey\u0026rsquo;s HSD was evaluated through visual inspection of residual plots and Levene\u0026rsquo;s test results. Potential order effects were controlled by including period as a fixed effect in all statistical models (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Model selection and the inclusion of random slopes were based on comparisons of likelihood ratio tests (LRT), Akaike\u0026rsquo;s Information Criterion (AIC), and Bayesian Information Criterion (BIC) (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Before model fitting, assumptions of linear regression (linearity, normality of residuals, and homoscedasticity) were assessed.\u003c/p\u003e \u003cp\u003eTo assess the potential mechanistic link between CPM effects and change in headache, two correlational approaches were used. Pearson correlation (\u003cem\u003er\u003c/em\u003e) was used to calculate the linear relationship between the continuous change in headache intensity and the continuous change in combined mean PPTs (post-condition minus pre-condition), performed separately for each condition group (MPT, CPT, and sham). Point-Biserial correlation (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003epb\u003c/em\u003e\u003c/sub\u003e) was used to assess the relationship between the change in headache intensity and the CPM-responder status. Participants were classified as responders if their mean combined PPTs increased by \u0026gt;\u0026thinsp;10% following the CPT. This threshold was anchored to the standard error of measurement (SEM) to ensure changes exceeded measurement noise (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). The primary assumption for the Pearson correlation, including linearity, was assessed through visual inspection of scatter plots. The assumption of approximate normality of the continuous variable within each group for the Point-Biserial correlation was also visually verified. Results for both correlations are shown with 95% CIs and p-values.\u003c/p\u003e \u003cp\u003eExploratory analyses for effect modification were conducted by including a three-way interaction term (condition, time, and candidate variable) into the mixed-effects models. Potential effect modifiers, including age, sex, headache-related disability, pain catastrophizing, and analgesic medication use, were assessed for their hypothesized influence on CPM as indicated by previous research (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). An effect modifier was considered relevant if the three-way interaction term was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) or if its inclusion changed the estimated treatment effect by \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;10% (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe statistical significance level for all analyses was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted using R (version 2024.04.02\u0026thinsp;+\u0026thinsp;764; R Foundation for Statistical Computing, Vienna, Austria). LMMs were implemented using the nlme package; post-hoc comparisons with Tukey\u0026rsquo;s HSD adjustments were conducted using the emmeans package. All data visualizations were generated using the ggplot2 package.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptives\u003c/h2\u003e \u003cp\u003eThirty-seven participants were included and completed the required pre- and post-condition assessments across the three study periods. The mean (SD) age was 46 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) years, and 70% was female. Detailed baseline characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline characteristics (n\u0026thinsp;=\u0026thinsp;37)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (29.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (70.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache Intensity Score-6 (36\u0026ndash;78) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePain Catastrophizing Scale (0\u0026ndash;52) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.30\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalgesic medication use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (75.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (24.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache history (years) (median (IQR))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache frequency (d/m) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.16\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Outcomes\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 PPTs on the trapezius muscle per condition group\u003c/h2\u003e \u003cp\u003eApplication of MPTs resulted in significantly higher PPTs compared to sham MPT (β\u0026thinsp;=\u0026thinsp;40.64, 95% CI [22.06, 59.23], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and differed significantly compared to CPT (β\u0026thinsp;=\u0026thinsp;7.2, 95% CI [-11.28, 25.68], p\u0026thinsp;=\u0026thinsp;0.45), indicating comparable effects of both conditions on PPTs. EMMs after conditions were 280.57 kPa (95% CI [254.05, 307.88]) for MPT, 282.62 kPa (95% CI [256.09, 309.15]) for CPT, and 254.58 kPa (95% CI [228.08, 281.08]) for sham MPT. Post-hoc comparisons confirmed that both MPT and CPT yielded significantly higher PPTs than sham (p\u0026thinsp;\u0026lt;\u0026thinsp;0.003). No statistically significant difference was found between MPT and CPT (p\u0026thinsp;=\u0026thinsp;0.96). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the distribution of PPT values per condition, both before and after the conditions. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visualizes the EMMs with their 95% CIs derived from the fitted model, illustrating pre- and post-condition changes for each condition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRaincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal; MPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal; MPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 PPTs on the tibialis anterior muscle per condition group\u003c/h2\u003e \u003cp\u003eApplication of MPTs resulted in significantly higher PPTs compared to sham MPT (MPT: β\u0026thinsp;=\u0026thinsp;25.31, 95% CI [5.00, 45.61], p\u0026thinsp;=\u0026thinsp;0.017). No statistically significant difference was found between MPTs and CPT (β\u0026thinsp;=\u0026thinsp;11.39, 95% CI [-8.81, 31.58], p\u0026thinsp;=\u0026thinsp;0.28), indicating comparable effects of both conditions on PPTs. EMMs after conditions were 293.27 kPa (95% CI [262.66, 323.87]) for MPT, 296.38 kPa (95% CI [265.76, 327.00]) for CPT, and 271.88 kPa (95% CI [241.29, 302.47]) for sham MPT. Post-hoc comparisons confirmed that both MPTs and CPT yielded significantly higher PPTs than sham (p\u0026thinsp;\u0026lt;\u0026thinsp;0.04). No statistically significant difference was found between MPT and CPT (p\u0026thinsp;=\u0026thinsp;0.93). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the distribution of PPT values per condition, both before and after conditions. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e visualizes the EMMs with their 95% CIs derived from the fitted model, illustrating pre- and post-condition changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRaincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal; MPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal; MPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 PPTs on the trapezius and tibialis anterior muscles combined per condition group\u003c/h2\u003e \u003cp\u003eMPTs resulted in significantly higher PPTs compared to sham MPT (β\u0026thinsp;=\u0026thinsp;32.50, 95% CI [17.05, 47.95], p\u0026thinsp;=\u0026thinsp;0.0001), while no statistical difference was found between the MPT and CPT application (β\u0026thinsp;=\u0026thinsp;2.12, 95% CI [-13.24, 17.49], p\u0026thinsp;=\u0026thinsp;0.79), indicating comparable effects of both conditions on PPTs. EMMs after conditions were 287.54 kPa (95% CI [263.18, 311.89]) for MPT, 289.43 kPa (95% CI [265.06, 313.79]) for CPT, and 263.68 kPa (95% CI [239.34, 288.01]) for sham MPT. Post-hoc comparisons confirmed that both the application MPTs and CPT yielded significantly higher PPTs than sham (p\u0026thinsp;\u0026lt;\u0026thinsp;0.002). No statistically significant difference was found between MPT and CPT (p\u0026thinsp;=\u0026thinsp;0.96). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the distribution of PPT values per condition, both before and after conditions. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e visualizes the EMMs with their 95% CIs derived from the fitted model, illustrating pre-and post-condition changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRaincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal; MPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal; MPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Headache intensity\u003c/h2\u003e \u003cp\u003eMPTs resulted in a significantly greater reduction in headache intensity compared to both CPT (β\u0026thinsp;=\u0026thinsp;1.42, 95% CI [0.78, 2.07]) and sham (β\u0026thinsp;=\u0026thinsp;1.74, 95% CI [1.10, 2.39]), with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003cp\u003ePost-condition EMMs were 2.00 (95% CI [1.08, 2.91]) for MPT, 2.94 (95% CI [2.02, 3.86]) for CPT, and 3.06 (95% CI [2.14, 3.98]) for sham. Pairwise differences showed no significant difference between CPT and sham (p\u0026thinsp;=\u0026thinsp;0.91), while the statistical difference between MPT and sham and between MPT and CPT was confirmed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.003). Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the distribution of NPRS scores before and after conditions, while Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents the EMMs with their 95% CIs derived from the fitted model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRaincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. MPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test; NPRS\u0026thinsp;=\u0026thinsp;numeric pain rating scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Correlations between change in headache intensity and change in PPTs\u003c/h2\u003e \u003cp\u003eNo statistically significant correlation was found between change in headache intensity and change in combined mean PPTs of the trapezius and tibialis in any of the groups (MPT: \u003cem\u003er\u003c/em\u003e = -0.05 (95% CI [-0.368, 0.278]), p\u0026thinsp;=\u0026thinsp;0.767; sham \u003cem\u003er\u003c/em\u003e = -0.179 (95% CI [-0.476, 0.154]), p\u0026thinsp;=\u0026thinsp;0.289; CPT: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.089 (95% CI [-0.242, 0.402)], p\u0026thinsp;=\u0026thinsp;0.599). The absence of significant correlations in each condition group is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, which plots change scores for mean PPTs and headache; a negative correlation indicates an increase in PPTs while the NPRS for headache decreases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal; MPT\u0026thinsp;=\u0026thinsp;manual pressure technique; CPT\u0026thinsp;=\u0026thinsp;cold pressor test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.2.6 Correlations between conditioned pain modulation responder status and change in headache intensity\u003c/h2\u003e \u003cp\u003eFollowing MPT, CPM responders demonstrated a significantly greater increase in PPTs compared to non-responders (β\u0026thinsp;=\u0026thinsp;19.94, 95% CI [2.36, 37.52], p\u0026thinsp;=\u0026thinsp;0.034). Post-condition EMMs were 295.16 kPa (95% CI [250.76, 339.56]) for non-responders and 281.10 kPa (95% CI [248.79, 313.40]) for responders. Post-hoc difference-in-difference analyses showed a statistical difference between responder and non-responder PPTs (estimate = -19.9, p\u0026thinsp;=\u0026thinsp;0.039), in favor of the responder group. Figure\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows the distribution of PPTs per responder status in the MPT group. The additional correlational analysis between CPM-responder status and change in headache intensity showed a statistically significant negative correlation in the MPT group (\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003epb\u003c/em\u003e\u003c/sub\u003e = -0.325, 95% CI [-0.587, -0.001], p\u0026thinsp;=\u0026thinsp;0.049). Figure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the distribution of change scores in NPRS in the MPT group, stratified by responder and non-responder status (defined as \u0026gt;\u0026thinsp;10% change in PPTs after CPT), where a negative value indicates a decrease in headache intensity while PPTs increase.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal. Raincloud plots display individual data points, half-density distribution, and boxplots. Diamond indicates group means. PPT\u0026thinsp;=\u0026thinsp;pressure pain threshold; kPa\u0026thinsp;=\u0026thinsp;kilopascal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNegative values indicate a decrease in headache while the PPT increases. Raincloud plots display individual data points, density distributions, and box plots. Black diamonds indicate group means. CPM\u0026thinsp;=\u0026thinsp;conditioned pain modulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.2.7 Effect modification\u003c/h2\u003e \u003cp\u003eEffect modification was assessed for several baseline covariates. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, multiple variables met the criteria for relevance (statistical significance and/or a change in effect estimate\u0026thinsp;\u0026gt;\u0026thinsp;10%), primarily driven by a change in effect estimate\u0026thinsp;\u0026gt;\u0026thinsp;10%, even when formal statistical significance for the three-way interaction was not always reached. For PPT, medication use showed both statistical significance and clinical relevance (p\u0026thinsp;=\u0026thinsp;0.003; 31.1%), which indicates that the condition effect was 31.3% higher in participants not using medication compared to those using analgesics. Sex also met the relevance threshold, with male participants showing a 12.5% larger improvement in PPT than females (p\u0026thinsp;=\u0026thinsp;0.116). Regarding headache intensity, the clinical relevance was most pronounced for sex (55.4%) and medication use (18.6%). Specifically, the reduction in NPRS scores was 55.4% greater in men and 18.6% larger in the non-medication group, although these interactions did not reach formal statistical significance. For CPM status, medication use (11.9%) and headache-related disability (20.7%) were identified as relevant modifiers. The effect of CPM status on the outcome was 11.9% stronger in participants without medication and 20.7% more pronounced in those with higher headache-related disability. In contrast, age and pain catastrophizing did not substantially modify the condition effects across any of the outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect modification on outcomes and candidate variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedication use\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePCS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHIT-6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.003; 31.1%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.116; 12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.075; 0.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.623; 2,2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.182; 1.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.156; 18.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.768; 55.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.330; 1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.169; 3.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.320; 0,4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCPM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.532; 11.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.367; 8.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.848; 7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.607; 2.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.678; 20.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e*Indicates statistical significance at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Percentages represent the percentage change of the effect estimate (derived from model comparison). PPT\u0026thinsp;=\u0026thinsp;pressure pain thresholds; CPM\u0026thinsp;=\u0026thinsp;conditioned pain modulation; PCS\u0026thinsp;=\u0026thinsp;pain catastrophizing scale; HIT-6\u0026thinsp;=\u0026thinsp;headache impact test-6.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Main findings\u003c/h2\u003e \u003cp\u003eThis randomized crossover trial demonstrated that MPTs significantly increased PPTs at local and distal sites, as well as the overall average, compared to sham, with an effect comparable to the CPT. Notably, while both MPT and CPT enhanced PPTs, only MPT produced a statistically significant reduction in headache intensity compared to sham and CPT. Furthermore, while headache reduction was not linearly correlated with the magnitude of PPT change, a significant linear association was found through the responder analysis. Participants classified as CPM responders demonstrated a significantly greater reduction in headache intensity following MPT than non-responders, along with a more pronounced increase in post-condition PPTs. Finally, exploratory effect modification analyses revealed that the absence of analgesic medication use, and male sex were the most consistent effect modifiers across outcomes. In contrast, age and pain catastrophizing did not substantially modify the condition effects across any of the outcomes.\u003c/p\u003e \u003cp\u003eThese findings suggest that while MPTs and CPT may activate descending pain modulatory pathways, their clinical impact differs. The comparable increase in PPTs across both conditions confirms that MPTs effectively engage pain modulatory pathways. These findings align with observations in healthy cohorts (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), mirroring both the direction and magnitude of those effects, and suggest that similar modulatory effects also occur in patients with CTTH. However, the lack of headache reduction following CPT suggests that headache reduction is not merely a function of CPM. This is further supported by the absence of a linear correlation between PPTs and headache intensity, although this likely reflects a restricted range, as participants reported minimal headache changes despite PPT variability (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Instead, the effect of MPT on headache might suggest a possible additional recruitment of localized neuromodulatory circuits within the trigeminal cervical complex (TCC), as discussed in previous studies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The significant role of CPM-responder status further elucidates this mechanism. The findings that responders derive more benefit from MPT indicate that these techniques may amplify existing adequate descending inhibition. Mechanistically, MPTs activate Aδ- and C-fiber afferents that trigger descending inhibition via the ventrolateral periaqueductal grey (vlPAG). While the CPT typically triggers a generalized, stress-induced analgesic response, MPTs may recruit more specific inhibitory pathways via the vlPAG and the rostral ventromedial medulla (RVM). These structures serve as key hubs for the descending control of nociception and are effectively engaged by manual interventions (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The effectiveness of MPTs in this study could possibly be explained by the topographical nature of such modulation, exerting segment-specific effects within the TCC (\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). This may be particularly relevant in chronic pain states such as CTTH, where central sensitization is characterized by facilitated synaptic plasticity (e.g., long-term potentiation in the dorsal horn C1-3). This increase in synaptic strength facilitates pain transmission even with normal afferent input (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Consequently, MPTs, acting as a localized mechanical stimulus, may be uniquely effective at tuning these sensitized synapses within the TCC. This spatial specificity explains why MPTs outperformed the CPT's distal nociceptive input in reducing headache intensity. These findings align with prior research indicating that distal stimuli primarily modulate distal pain, whereas proximal stimuli are more effective in proximal regions (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Furthermore, the observation that effect modifiers influenced outcomes variably suggests that MPT responsiveness is driven by the patient\u0026rsquo;s dynamic physiological state rather than isolated, static traits. This aligns with the inconsistent findings in existing literature suggesting that a higher cognitive-affective burden and polypharmacy may blunt responsiveness through impaired descending inhibition or altered central sensitization (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Such variability further supports the neurophysiological framework by Hoegh and Bannister (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), which posits that the descending pain modulatory system is a highly plastic network sensitive to immediate contextual and physiological fluctuations. Consequently, static trait-level questionnaires may be insufficient to capture the momentary modulatory capacity required to assess effect modification (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). This is reflected in the current findings, where psychological traits such as pain catastrophizing failed to meaningfully modify the treatment effect. In contrast, medication use and sex, representing more direct physiological factors, emerged as more robust modifiers. It is therefore possible that real-time, state-dependent assessments would yield different or more consistent moderation effects than the baseline trait measurements used in the current study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Strengths and limitations\u003c/h2\u003e \u003cp\u003eOur study contains some limitations. First, the sample size may have been underpowered to detect complex moderation interactions involving continuous variables and multi-level model structures, such as autoregressive structures and random effects. This increases the risk of unstable estimates and inflation of error rates, particularly when changes are numerically small but proportionally large (\u003cspan additionalcitationids=\"CR60\" citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Furthermore, while the use of a threshold to define CPM-responder is essential to distinguish true physiological change from measurement noise, the application of a fixed 10% cut-off represents a potential limitation. Fixed percentage-based thresholds are common in clinical research (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e), yet they remain somewhat arbitrary and may lead to misclassification if they do not account for study-specific variability (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). To mitigate this, we anchored our 10% cut-off to the SEM derived from our baseline PPT measurements. By using a threshold supported by absolute reliability metrics rather than a purely arbitrary percentage, we minimized the risk of overestimating the CPM effect due to natural variability and enhanced our ability to distinguish true physiological modulation from measurement error (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e A strength of this study is its rigorous randomized crossover design, conducted in accordance with CONSORT guidelines. By utilizing participants as their own controls and incorporating a washout period, the design minimized between-subject variability, (temporal) confounding, and potential carry-over effects. The internal validity was further reinforced by multi-level blinding, encompassing participants, outcome assessors, and data analysts. While the nature of the intervention precluded clinician blinding, the separation of research roles and the inclusion of a sham condition mitigated expectancy effects and controlled for the non-specific effects of manual contact. This methodological rigor extended to the analytical framework; we employed LMMs with autoregressive structures and random intercepts, while controlling for period effects, to account for repeated measures and temporal dependencies inherent in the crossover design. The use of EMMs and visualizations subsequently allowed for the interpretation of treatment-by-time interactions. Furthermore, the application of a standardized and validated CPM protocol (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) ensured high construct validity, aligning our findings with current gold standards in experimental pain research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Implications\u003c/h2\u003e \u003cp\u003eThe findings of this study suggest that MPTs may offer clinically meaningful benefits for individuals with CTTH, demonstrating that manual interventions remain effective even in people with CTTH, where pain modulation often is impaired. Notably, the comparable increases in PPTs following both MPT and CPT suggest that manual techniques are as effective in engaging descending pain-modulatory pathways. This underscores the possible therapeutic value of MPTs for activating endogenous pain control, despite the neuroplastic changes associated with chronic pain states. Furthermore, screening for CPM-responder status in clinical practice and research may help identify individuals who are most likely to respond to MPT treatment, serving as a pragmatic tool to guide personalized treatment planning and increase effect sizes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Recommendations\u003c/h2\u003e \u003cp\u003eBased on the findings and limitations of the current study, several possibilities for future research are proposed to advance the clinical application of MPTs in people with CTTH.\u003c/p\u003e \u003cp\u003eFirst, while this study provides preliminary insights into treatment response, the sample size may have constrained the detection of subtle moderator effects within the complex multi-level model structure. Future research should employ larger cohorts to validate candidate effect modifiers, prioritizing state-based physiological assessments over conventional trait-level questionnaires.\u003c/p\u003e \u003cp\u003eSecond, there is a need for methodological standardization. Future studies should focus on determining population-specific reliability metrics, such as the SEM and SDC, for defining responder criteria in CTTH that distinguish true neurophysiological changes from inherent measurement noise. Establishing these thresholds will facilitate the development of CPM-responder profiles, supporting a transition toward more personalized and mechanism-informed physiotherapy strategies.\u003c/p\u003e \u003cp\u003eFinally, future research should investigate the dose-response relationships of MPTs by varying treatment, such as dosage, intensity, and frequency of MPTs. Incorporating extended follow-up periods will help to evaluate the longitudinal durability of these neuromodulatory effects and to better understand the temporal dynamics of neuroplastic adaptation in CTTH.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study demonstrates that MPTs effectively engage descending pain modulatory pathways in individuals with CTTH, yielding physiological effects comparable to the CPT. However, while both conditions enhanced PPTs, only MPTs led to a clinically significant reduction in headache intensity. This improvement was not linearly correlated with the magnitude of PPT change but was instead significantly associated with individual CPM-responder status. While certain baseline factors modified effect estimates, the variability across outcomes suggests that results are primarily driven by the patient\u0026rsquo;s dynamic physiological state. Overall, the results seem to support the clinical utility of MPTs in managing CTTH and provide mechanistic insights that may inform future research and the development of personalized, mechanism-informed physiotherapy strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCTTH: Chronic tension-type headache; MPT: Manual pressure technique; CPT: Cold pressor test; PPT: Pressure pain threshold; NPRS: Numeric pain rating scale; CPM: Conditioned pain modulation; LMM: Linear mixed-effects model; CI: Confidence interval; kPa: kilopascal; EMM: estimated marginal mean.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the MSG Science Netwerk Fysiotherapie for their support of this project and all participants for their time and contribution to this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.1 Ethics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was approved by the Ethics Committee of the Faculty of Behavioral and Movement Sciences, Vrije Universiteit Amsterdam (reference number: VCWE-2025-015). Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6.2 Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6.3 Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e6.4 Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6.5 Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6.6 Authors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBtM: study concept and design, acquisition of data, statistical analysis and interpretation of data, and preparation of the manuscript. RC: study concept and design, acquisition of data, statistical analysis and interpretation of data, and preparation of the manuscript. XZ: study concept and design, acquisition of data, and preparation of the manuscript. BvdM: study concept and design, preparation of the manuscript. GSP: study concept and design, preparation of the manuscript. All authors have read and approved the final manuscript. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYu S, Han X (2015) Update of Chronic Tension-Type Headache. Curr Pain Headache Rep January 19(1):469. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11916-014-0469-5\u003c/span\u003e\u003cspan address=\"10.1007/s11916-014-0469-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshina S, Buse DC, Bjorner JB, Bendtsen L, Lyngberg AC, Jensen RH (2021) e.a. Health-related quality of life in tension-type headache: a population-based study. Scandinavian Journal of Pain. 26 October ;21(4):778\u0026thinsp;\u0026ndash;\u0026thinsp;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1515/sjpain-2020-0166\u003c/span\u003e\u003cspan address=\"10.1515/sjpain-2020-0166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan LLH, Ling YH, Wang SJ, Al-Hassany L, Chen WT, Chiang CC (2025) e.a. Hallmarks of primary headache: part 2\u0026ndash; Tension-type headache. J Headache Pain. 17 July ;26(1):164. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s10194-025-02098-w\u003c/span\u003e\u003cspan address=\"10.1186/s10194-025-02098-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastien R, De Hertogh W (2019) A Neuroscience Perspective of Physical Treatment of Headache and Neck Pain. Front Neurol 26 march 10:276. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2019.00276\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2019.00276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Hertogh W, Amons A, Van Daele L, Vanbaelen E, Castien R (October 2022) Modulation of Central Nociceptive Transmission by Manual Pressure Techniques in Patients with Migraine: An Observational Study. JCM 25(21):6273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm11216273\u003c/span\u003e\u003cspan address=\"10.3390/jcm11216273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDo TP, Heldarskard GF, Kolding LT, Hvedstrup J, Schytz HW (2018) Myofascial trigger points in migraine and tension-type headache. J Headache Pain Dec 19(1):84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s10194-018-0913-8\u003c/span\u003e\u003cspan address=\"10.1186/s10194-018-0913-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatson DH, Drummond PD (2012) Head Pain Referral During Examination of the Neck in Migraine and Tension-Type Headache. Headache September 52(8):1226\u0026ndash;1235. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1526-4610.2012.02169.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1526-4610.2012.02169.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshina M (2004) Neurobiology of Chronic Tension-Type Headache. Cephalalgia March 24(3):161\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1468-2982.2003.00644.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1468-2982.2003.00644.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastien R, Duineveld D, De Hertogh W, Scholten-Peeters G (2021) Pericranial Total Tenderness Score in Patients with Tension-type Headache and Migraine: A Systematic Review and Meta- analysis. Pain Physician. ;24(8)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePensri C, Liang Z, Treleaven J, Jull G, Thomas L (2025) Cervical musculoskeletal impairments in migraine and tension-type headache and relationship to pain related factors: An updated systematic review and meta-analysis. Musculoskelet Sci Pract April 76:103251. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.msksp.2024.103251\u003c/span\u003e\u003cspan address=\"10.1016/j.msksp.2024.103251\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerranz-G\u0026oacute;mez A, Garc\u0026iacute;a-Pascual I, Montero-Iniesta P, Touche RL, Paris-Alemany A (2021) Effectiveness of Exercise and Manual Therapy as Treatment for Patients with Migraine, Tension-Type Headache or Cervicogenic Headache: An Umbrella and Mapping Review with Meta-Meta-Analysis. Appl Sci 26 July 11(15):6856. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/app11156856\u003c/span\u003e\u003cspan address=\"10.3390/app11156856\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJung A, Eschke RC, Struss J, Taucher W, Luedtke K (2022) Effectiveness of physiotherapy interventions on headache intensity, frequency, duration and quality of life of patients with tension-type headache. A systematic review and network meta-analysis. Cephalalgia August 42(9):944\u0026ndash;965. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/03331024221082073\u003c/span\u003e\u003cspan address=\"10.1177/03331024221082073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026oslash;ll LS, Callesen HE, Carlsen LN, Birkefoss K, Beier D, Christensen HW (2021) e.a. Manual joint mobilisation techniques, supervised physical activity, psychological treatment, acupuncture and patient education for patients with tension-type headache. A systematic review and meta-analysis. J Headache Pain. December ;22(1):96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s10194-021-01298-4\u003c/span\u003e\u003cspan address=\"10.1186/s10194-021-01298-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang W, Li Z, Wei N, Chang W, Chen W, Sui HJ (2019) Effectiveness of physical therapy on the suboccipital area of patients with tension-type headache: A meta-analysis of randomized controlled trials. Med May 98(19):e15487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000015487\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000015487\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLascurain-Aguirrebe\u0026ntilde;a I, Newham D, Critchley DJ (2016) Mechanism of Action of Spinal Mobilizations: A Systematic Review. SPINE January 41(2):159\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/BRS.0000000000001151\u003c/span\u003e\u003cspan address=\"10.1097/BRS.0000000000001151\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArribas-Romano A, Fern\u0026aacute;ndez-Carnero J, Rodr\u0026iacute;guez-Lagos L, Molina-\u0026Aacute;lvarez M, Zabala-Zambrano J, Lezaun-Hern\u0026aacute;ndez L (2024) e.a. CPM-Related Mechanisms Could Play a Key Role in the Effects on Pain Sensitivity Induced by Manual Therapy: Three Crossover Trials Investigating the Effects of Manual Pressure. JCM. 22 June ;13(13):3648. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/jcm13133648\u003c/span\u003e\u003cspan address=\"10.3390/jcm13133648\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastien R, Reezigt RR, Den Hartog R, Amons A, De Hertogh W, Scholten-Peeters GG (April 2025) Conditioned pain modulation elicited through manual pressure techniques on the cervical spine: a crossover study. PR9. 10(2):e1258. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/PR9.0000000000001258\u003c/span\u003e\u003cspan address=\"10.1097/PR9.0000000000001258\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNir RR, Yarnitsky D (June 2015) Conditioned pain modulation. Current Opinion in Supportive \u0026amp; Palliative Care. 9(2):131\u0026ndash;137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/SPC.0000000000000126\u003c/span\u003e\u003cspan address=\"10.1097/SPC.0000000000000126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamaswamy S, Wodehouse T (2021) Conditioned pain modulation\u0026mdash;A comprehensive review. Neurophysiologie Clinique June 51(3):197\u0026ndash;208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neucli.2020.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.neucli.2020.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams AE, Miller MM, Bartley EJ, McCabe KM, Kerr KL, Rhudy JL (2019) Impairment of Inhibition of Trigeminal Nociception via Conditioned Pain Modulation in Persons with Migraine Headaches. Pain Med 1 August 20(8):1600\u0026ndash;1610. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/pm/pny305\u003c/span\u003e\u003cspan address=\"10.1093/pm/pny305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStaud R (2013) The important role of CNS facilitation and inhibition for chronic pain. Int J Clin Rheumatol Dec 8(6):639\u0026ndash;646. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2217/ijr.13.57\u003c/span\u003e\u003cspan address=\"10.2217/ijr.13.57\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExposto FG, Bendixen KH, Ernberg M, Bach FW, Svensson P (2021) Assessment of Pain Modulatory and Somatosensory Profiles in Chronic Tension-Type Headache Patients. Pain Med 8 Oct 22(10):2356\u0026ndash;2365. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/pm/pnab084\u003c/span\u003e\u003cspan address=\"10.1093/pm/pnab084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis GN, Rice DA, McNair PJ (2012) Conditioned Pain Modulation in Populations With Chronic Pain: A Systematic Review and Meta-Analysis. J Pain Oct 13(10):936\u0026ndash;944. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpain.2012.07.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jpain.2012.07.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeri T, Viceconti A, Minacci M, Testa M, Rossettini G (2019) Manual therapy: Exploiting the role of human touch. Musculoskelet Sci Pract Dec 44:102044. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.msksp.2019.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.msksp.2019.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwan K, Li T, Altman DG, Elbourne D CONSORT 2010 statement: extension to randomised crossover trials. BMJ. 31 July 2019;l4378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.l4378\u003c/span\u003e\u003cspan address=\"10.1136/bmj.l4378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBiurrun Manresa JA, Fritsche R, Vuilleumier PH, Oehler C, M\u0026oslash;rch CD, Arendt-Nielsen L (2014) e.a. Is the Conditioned Pain Modulation Paradigm Reliable? A Test-Retest Assessment Using the Nociceptive Withdrawal Reflex. Zuo XN. PLoS ONE. 20 June ;9(6):e100241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0100241\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0100241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReezigt RR, Kielstra SC, Coppieters MW, Scholten-Peeters GGM (2021) No relevant differences in conditioned pain modulation effects between parallel and sequential test design. A cross-sectional observational study. PeerJ 14 Dec 9:e12330. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7717/peerj.12330\u003c/span\u003e\u003cspan address=\"10.7717/peerj.12330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIbancos-Losada MDR, Osuna-P\u0026eacute;rez MC, Castellote-Caballero MY, D\u0026iacute;az-Fern\u0026aacute;ndez \u0026Aacute; (2020) Conditioned Pain Modulation Effectiveness: An Experimental Study Comparing Test Paradigms and Analyzing Potential Predictors in a Healthy Population. Brain Sci 30 August 10(9):599. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/brainsci10090599\u003c/span\u003e\u003cspan address=\"10.3390/brainsci10090599\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLewis GN, Luke H, Rice DA, Rome K, McNair PJ (2012) Reliability of the Conditioned Pain Modulation Paradigm to Assess Endogenous Inhibitory Pain Pathways. Pain Res Manage January 17(2):98\u0026ndash;102. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2012/610561\u003c/span\u003e\u003cspan address=\"10.1155/2012/610561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung IA, Dunning J, Butts R, Cleland JA, Fern\u0026aacute;ndez-de-las-Pe\u0026ntilde;as C (2019) Psychometric properties of the Numeric Pain Rating Scale and Neck Disability Index in patients with cervicogenic headache. Cephalalgia January 39(1):44\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0333102418772584\u003c/span\u003e\u003cspan address=\"10.1177/0333102418772584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin M, Blaisdell B, Kwong JW, Bjorner JB (2004) The Short-Form Headache Impact Test (HIT-6) was psychometrically equivalent in nine languages. J Clin Epidemiol Dec 57(12):1271\u0026ndash;1278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jclinepi.2004.05.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jclinepi.2004.05.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSevereijns R, Van Den Hout MA, Vlaeyen JWS, Picavet SJH (2002) Pain catastrophizing and general health status in a large Dutch community sample. Pain September 99(1):367\u0026ndash;376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0304-3959(02)00219-1\u003c/span\u003e\u003cspan address=\"10.1016/S0304-3959(02)00219-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZieliński G (2025) Effect Size Guidelines for Individual and Group Differences in Physiotherapy. Archives Phys Med Rehabilitation Dec 106(12):1844\u0026ndash;1849. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.apmr.2025.05.013\u003c/span\u003e\u003cspan address=\"10.1016/j.apmr.2025.05.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltman DG, Bland JM (1995) Statistics notes: The normal distribution. BMJ 4 Febr 310(6975):298\u0026ndash;298. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.310.6975.298\u003c/span\u003e\u003cspan address=\"10.1136/bmj.310.6975.298\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFitzmaurice GM, Laird NM, Ware JH (2011) Applied longitudinal analysis. 2nd ed. Hoboken, N.J: Wiley; 701 p. (Wiley series in probability and statistics)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTwisk JWR (2013) Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide [Internet]. 2de ed. Cambridge University Press; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1017/CBO9781139342834\u003c/span\u003e\u003cspan address=\"10.1017/CBO9781139342834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eField A, Miles J, Field Z (2014) Discovering statistics using R. Repr. Sage, Los Angeles, CA, USA, p 957\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaxwell SE, Delaney HD, Kelley K (2017) Designing Experiments and Analyzing Data: A Model Comparison Perspective [Internet]. 3de dr. Third edition / Scott E. Maxwell, Harold D. Delaney, and Ken Kelley. New York,: Routledge; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4324/9781315642956\u003c/span\u003e\u003cspan address=\"10.4324/9781315642956\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones B, Kenward MG (2003) Design and Analysis of Cross-Over Trials [Internet]. ed. Chapman and Hall/CRC, p 0. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1201/9781420036091\u003c/span\u003e\u003cspan address=\"10.1201/9781420036091\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnham KP, Anderson DR, Burnham KP (2002) Model selection and multimodel inference: a practical information-theoretic approach, 2nd edn. Springer, New York, p 488\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Vet HCW, Terwee CB, Knol DL, Bouter LM (2006) When to use agreement versus reliability measures. J Clin Epidemiol Oct 59(10):1033\u0026ndash;1039. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jclinepi.2005.10.015\u003c/span\u003e\u003cspan address=\"10.1016/j.jclinepi.2005.10.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWyrwich KW (2004) Minimal Important Difference Thresholds and the Standard Error of Measurement: Is There a Connection? J Biopharm Stat 24 Dec 14(1):97\u0026ndash;110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1081/BIP-120028508\u003c/span\u003e\u003cspan address=\"10.1081/BIP-120028508\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHermans L, Van Oosterwijck J, Goubert D, Goudman L, Crombez G, Calders P (2016) e.a. Inventory of Personal Factors Influencing Conditioned Pain Modulation in Healthy People: A Systematic Literature Review. Pain Practice. July ;16(6):758\u0026thinsp;\u0026ndash;\u0026thinsp;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/papr.12305\u003c/span\u003e\u003cspan address=\"10.1111/papr.12305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMertens MG, Hermans L, Crombez G, Goudman L, Calders P, Van Oosterwijck J (2021) e.a. Comparison of five conditioned pain modulation paradigms and influencing personal factors in healthy adults. European Journal of Pain. January ;25(1):243\u0026thinsp;\u0026ndash;\u0026thinsp;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ejp.1665\u003c/span\u003e\u003cspan address=\"10.1002/ejp.1665\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNahman-Averbuch H, Nir RR, Sprecher E, Yarnitsky D (2016) Psychological Factors and Conditioned Pain Modulation: A Meta-Analysis. Clin J Pain June 32(6):541\u0026ndash;554. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/AJP.0000000000000296\u003c/span\u003e\u003cspan address=\"10.1097/AJP.0000000000000296\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Briel M, Walter SD, Guyatt GH (2010) Is a subgroup effect believable? Updating criteria to evaluate the credibility of subgroup analyses. BMJ 30 March 340(mar30 3):c117\u0026ndash;c117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.c117\u003c/span\u003e\u003cspan address=\"10.1136/bmj.c117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBland JM, Altman DG (2011) Correlation in restricted ranges of data. BMJ 11 March 342(mar11 1):d556\u0026ndash;d556. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.d556\u003c/span\u003e\u003cspan address=\"10.1136/bmj.d556\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodwin LD, Leech NL (2006) Understanding Correlation: Factors That Affect the Size of r. J Experimental Educ April 74(3):249\u0026ndash;266. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3200/JEXE.74.3.249-266\u003c/span\u003e\u003cspan address=\"10.3200/JEXE.74.3.249-266\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoegh M, Bannister K (2024) Pain Science in Practice (Part 6): \u003cem\u003eHow Does Descending Modulation of Pain Work?\u003c/em\u003e. J Orthop Sports Phys Therapy Febr 54(2):97\u0026ndash;100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2519/jospt.2024.12112\u003c/span\u003e\u003cspan address=\"10.2519/jospt.2024.12112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinnman C, Moulton EA, Barmettler G, Becerra L, Borsook D (2012) Neuroimaging of the periaqueductal gray: State of the field. NeuroImage March 60(1):505\u0026ndash;522. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2011.11.095\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2011.11.095\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehnert J, Tinnermann A, Basedau H, May A (2024) Functional representation of trigeminal nociceptive input in the human periaqueductal gray. Sci Adv 22 March 10(12):eadj8213. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.adj8213\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.adj8213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTinoco Mendoza FA, Hughes TES, Robertson RV, Crawford LS, Meylakh N, Macey PM (2023) e.a. Detailed organisation of the human midbrain periaqueductal grey revealed using ultra-high field magnetic resonance imaging. NeuroImage. February ;266:119828. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.neuroimage.2022.119828\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2022.119828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoegh M (2023) Pain Science in Practice (Part 5): \u003cem\u003eCentral Sensitization II\u003c/em\u003e. J Orthop Sports Phys Therapy Febr 53(2):55\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2519/jospt.2023.11571\u003c/span\u003e\u003cspan address=\"10.2519/jospt.2023.11571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoegh M (2023) Pain Science in Practice (Part 4): \u003cem\u003eCentral Sensitization I\u003c/em\u003e. J Orthop Sports Phys Therapy January 53(1):1\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2519/jospt.2023.11569\u003c/span\u003e\u003cspan address=\"10.2519/jospt.2023.11569\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranovsky Y, Sprecher E, Yarovinsky N, Shor M, Crystal S (2024) Body-site effect on CPM efficiency in healthy subjects: Central vs. peripheral stimulation. Heliyon Febr 10(3):e25156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2024.e25156\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e25156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoegh M, Bannister K (2024) Pain Science in Practice (Part 7): \u003cem\u003eHow Is Descending Modulation of Pain Measured?\u003c/em\u003e. J Orthop Sports Phys Therapy March 54(3):155\u0026ndash;160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2519/jospt.2024.12113\u003c/span\u003e\u003cspan address=\"10.2519/jospt.2024.12113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillan MJ (2002) Descending control of pain. Progress Neurobiol April 66(6):355\u0026ndash;474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0301-0082(02)00009-6\u003c/span\u003e\u003cspan address=\"10.1016/S0301-0082(02)00009-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYarnitsky D, Arendt-Nielsen L, Bouhassira D, Edwards RR, Fillingim RB, Granot M (2010) e.a. Recommendations on terminology and practice of psychophysical DNIC testing. European Journal of Pain. April ;14(4):339\u0026ndash;339. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejpain.2010.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.ejpain.2010.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee PH (2014) Is a Cutoff of 10% Appropriate for the Change-in-Estimate Criterion of Confounder Identification? J Epidemiol 24(2):161\u0026ndash;167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2188/jea.JE20130062\u003c/span\u003e\u003cspan address=\"10.2188/jea.JE20130062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeon AC, Heo M (2009) Sample sizes required to detect interactions between two binary fixed-effects in a mixed-effects linear regression model. Comput Stat Data Anal January 53(3):603\u0026ndash;608. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.csda.2008.06.010\u003c/span\u003e\u003cspan address=\"10.1016/j.csda.2008.06.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaas CJM, Hox JJ (2005) Sufficient Sample Sizes for Multilevel Modeling. Methodol January 1(3):86\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1027/1614-2241.1.3.86\u003c/span\u003e\u003cspan address=\"10.1027/1614-2241.1.3.86\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLocke D, Gibson W, Moss P, Munyard K, Mamotte C, Wright A (2014) Analysis of Meaningful Conditioned Pain Modulation Effect in a Pain-Free Adult Population. J Pain November 15(11):1190\u0026ndash;1198. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpain.2014.09.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jpain.2014.09.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYarnitsky D, Crispel Y, Eisenberg E, Granovsky Y, Ben-Nun A, Sprecher E (2008) e.a. Prediction of chronic post-operative pain: Pre-operative DNIC testing identifies patients at risk. Pain. 15 August ;138(1):22\u0026thinsp;\u0026ndash;\u0026thinsp;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pain.2007.10.033\u003c/span\u003e\u003cspan address=\"10.1016/j.pain.2007.10.033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtkinson G, Nevill AM (1998) Statistical Methods For Assessing Measurement Error (Reliability) in Variables Relevant to Sports Medicine. Sports Med 26(4):217\u0026ndash;238. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2165/00007256-199826040-00002\u003c/span\u003e\u003cspan address=\"10.2165/00007256-199826040-00002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKennedy DL, Kemp HI, Ridout D, Yarnitsky D, Rice ASC (2016) Reliability of conditioned pain modulation: a systematic review. Pain November 157(11):2410\u0026ndash;2419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/j.pain.0000000000000689\u003c/span\u003e\u003cspan address=\"10.1097/j.pain.0000000000000689\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKennedy DL, Kemp HI, Wu C, Ridout DA, Rice ASC (2020) Determining Real Change in Conditioned Pain Modulation: A Repeated Measures Study in Healthy Volunteers. J Pain May 21(5\u0026ndash;6):708\u0026ndash;721. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpain.2019.09.010\u003c/span\u003e\u003cspan address=\"10.1016/j.jpain.2019.09.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeir JP (2005) Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Conditioning Res 19(1):231\u0026ndash;240\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Flowchart 1","content":"\u003cp\u003eFlowchart 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"chronic tension-type headache, conditioned pain modulation, inhibitory control, central sensitization, manual pressure techniques","lastPublishedDoi":"10.21203/rs.3.rs-9222006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9222006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eChronic tension-type headache (CTTH) is characterized by central sensitization and impaired descending pain modulation. Manual pressure techniques are hypothesized to engage descending pain-modulatory pathways, a mechanism quantifiable through conditioned pain modulation protocols. We investigated whether manual pressure techniques activate these pathways and compared their efficacy with the cold pressor test.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this randomized crossover trial, thirty-seven participants with CTTH received three conditions: manual pressure techniques, sham techniques, and the cold pressor test. Primary outcomes were pressure pain thresholds at the trapezius and tibialis anterior muscles, and headache intensity. Data were analyzed using linear mixed-effects models. Secondary analysis examined the association between conditioned pain modulation responder status and clinical outcomes. An exploratory analysis was performed to assess effect modification.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBoth manual pressure techniques and the cold pressor test significantly increased global pressure pain thresholds compared to sham (p\u0026thinsp;\u0026lt;\u0026thinsp;0.002). There was no significant difference between threshold increases between manual pressure techniques and the cold pressor test (p\u0026thinsp;=\u0026thinsp;0.96), suggesting comparable activation of modulatory mechanisms. Notably, only manual pressure techniques resulted in a significantly greater reduction in headache intensity compared to both the sham and the cold pressor test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Headache reduction did not correlate linearly with changes in pressure pain thresholds (p\u0026thinsp;\u0026gt;\u0026thinsp;0.59). Responder status was significantly associated with outcomes: responders demonstrated a larger pressure pain threshold increase (p\u0026thinsp;=\u0026thinsp;0.039) and superior headache relief (p\u0026thinsp;=\u0026thinsp;0.049) than non-responders. Exploratory analyses identified male sex and absence of analgesic medication as relevant effect modifiers.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eManual pressure techniques reduce pain sensitivity, indicating activation of descending pain-modulatory pathways in CTTH, with effects comparable to those observed with the cold pressor test. However, the unique efficacy of manual pressure techniques in reducing headache intensity suggests clinical benefits beyond generalized noxious inhibitory control. These findings support manual pressure techniques as a mechanism-informed intervention for CTTH.\u003c/p\u003e","manuscriptTitle":"Manual Pressure Techniques Activate Descending Pain-Modulatory Pathways and Reduce Headache Intensity in Chronic Tension-Type Headache: A Randomized Crossover Trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-28 16:28:28","doi":"10.21203/rs.3.rs-9222006/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":"3b0b4142-e63a-4dee-97b4-a63d30b775a8","owner":[],"postedDate":"March 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-28T16:28:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-28 16:28:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9222006","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9222006","identity":"rs-9222006","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}