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Background:
Systematic Review

Do Pain and Autonomic Regulation Share a Common Central Compensatory Pathway? A Meta-Analysis of HRV Metrics in Pain Trials

by
Marianna Daibes
1,†,
Bassel Almarie
1,†,
Maria Fernanda Andrade
1,
Giovanna de Paula Vidigal
1,2,
Nadine Aranis
1,
Anna Gianlorenco
1,3,
Carlos Bandeira de Mello Monteiro
2,
Prateek Grover
4,
David Sparrow
5 and
Felipe Fregni
1,*
1
Neuromodulation Center and Center for Clinical Research Learning, Spaulding Rehabilitation Hospital and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02138, USA
2
Department of Physical Therapy, Speech and Occupational Therapy, Faculty of Medicine, University of São Paulo (USP), Sao Paulo 05508-220, Brazil
3
Department of Physical Therapy, Federal University of Sao Carlos, Sao Paulo 13565-905, Brazil
4
Limb Loss, Difference, and Preservation Rehabilitation Center for Research, Education, and Service (LimbRehabCaRES), Penn State College of Medicine, Hershey, PA 17033, USA
5
Department of Medicine, Boston University Chobanian and Avedisian School of Medicine, and VA Normative Aging Study, Veterans Affairs Boston Healthcare System, Boston, MA 02118, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
NeuroSci 2025, 6(3), 62; https://doi.org/10.3390/neurosci6030062 (registering DOI)
Submission received: 11 May 2025 / Revised: 17 June 2025 / Accepted: 27 June 2025 / Published: 5 July 2025
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Abstract

Background: Chronic pain is closely associated with dysregulation of the autonomic nervous system, often reflected by reduced heart rate variability (HRV). While observational studies have demonstrated this association, the extent to which pain interventions modulate HRV and the impact of individual factors on HRV changes remain unclear. Objective: To evaluate the impact of pain interventions on HRV parameters through meta-analysis of randomized controlled trials (RCTs), and to examine whether intervention type and individual factors such as body mass index (BMI) moderate HRV responses. Methods: We conducted a systematic review of 23 RCTs and a meta-analysis of 21 RCTs (1262 subjects) involving patients with acute and chronic pain. HRV outcomes were extracted pre- and post-intervention. Both between-group (active vs. sham/control) and one-group (pre-post within active group) analyses were performed for time-domain indices—standard deviation of normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD), and percentage of successive normal-to-normal intervals >50 ms (pNN50)—and frequency-domain indices—high-frequency (HF) and low-frequency (LF) components. Meta-regressions tested moderators including BMI, age, and pain phenotype. The protocol was registered in PROSPERO (CRD42023448264). Results: Twenty-three RCTs involving 1262 participants with a wide range of pain conditions were included. Meta-analysis of time-domain HRV parameters showed a trend toward improvement: SDNN (g = 0.435, p = 0.059) approached significance, while RMSSD (g = 0.361, p = 0.099) and pNN50 (g = 0.222, p = 0.548) showed smaller, non-significant effects. Frequency-domain analysis revealed a significant moderate reduction in the LF/HF ratio (g = −0.378, p = 0.003), suggesting a shift toward parasympathetic dominance. HF and LF showed small, non-significant changes. One-group meta-analysis confirmed significant improvements in vagally mediated HRV, with large effects for RMSSD (g = 1.084, p < 0.001) and HF (g = 0.622, p < 0.001), and a moderate effect for SDNN (g = 0.455, p = 0.004). Meta-regression identified BMI as a significant moderator: higher BMI was associated with attenuated improvements in HF and RMSSD and a slight shift toward sympathetic predominance. Conclusions: Pain interventions can significantly modulate autonomic function, as reflected in HRV improvements, particularly in vagally mediated indices. These effects are influenced by patient characteristics such as BMI. HRV may serve as a valuable biomarker for both treatment efficacy and autonomic recovery in pain management. In this context, HRV highlights its role as a biomarker for pain dysregulation and compensatory failure, reflecting shared top-down modulation between nociception and autonomic regulation.

1. Introduction

Pain imposes a substantial burden on health and longevity [1,2]. Among individuals under 50 years of age, chronic pain is associated with a 2.5- to 3-fold increase in mortality compared to the general population, underscoring the serious and often underrecognized consequences of persistent pain [3,4]. In diseases characterized by pain involving both central and peripheral mechanisms—such as systemic lupus erythematosus, rheumatoid arthritis (RA), and fibromyalgia [5,6,7]—autonomic nervous system (ANS) dysfunction, reflected by reduced heart rate variability (HRV), has been consistently observed. HRV indices can be categorized into time-domain and frequency-domain, and non-linear categories.
Importantly, this autonomic imbalance, represented by HRV, appears to be linked more directly to inflammation than to pain intensity alone. For instance, in patients with systemic lupus erythematosus, lower HRV indices—particularly sample entropy—were inversely associated with levels of inflammatory biomarkers such as high-sensitivity C-reactive protein (hsCRP) and myeloperoxidase, suggesting a relationship between systemic inflammation and impaired cardiac autonomic control, independent of disease activity or pain [8,9]. Supporting this, Koopman et al. (2016) demonstrated in both preclinical and clinical studies that autonomic dysregulation precedes rheumatoid arthritis onset and is predictive of disease development [10]. In rheumatoid arthritis patients, reduced parasympathetic tone, as measured by HRV, was correlated with higher levels of inflammatory cytokines like tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6). Moreover, individuals with higher vagal tone responded better to anti-TNF therapies, indicating that autonomic balance may modulate inflammatory responses [11,12]. Similarly, imaging and physiological studies in patients with chronic inflammatory joint diseases have highlighted the role of the insular cortex—a brain region implicated in both pain perception and autonomic regulation. Alterations in insular cortex volume correlated with heart rate changes and were proposed as neural correlates of dysregulated autonomic nervous system function in chronic pain states [13].
These insights support the hypothesis that autonomic abnormalities (e.g., reduced HRV) and chronic pain may not be causally linear, but instead parallel manifestations of a shared deregulatory process involving immune signaling, central nervous system plasticity, and impaired vagal tone [14,15]. Observational studies find lower HRV in chronic pain, but intervention trials are inconsistent, often ignore HF and LF/HF, and rarely test moderators such as BMI or pain phenotype [16,17]. Given the evidence that inflammation-driven neural and microvascular injury contributes to both persistent pain and autonomic dysfunction [18,19], we aim to systematically evaluate whether clinical interventions that alleviate pain also restore autonomic balance, as reflected in HRV metrics. Therefore, the objective of this meta-analysis is to assess randomized controlled trials of pain interventions to answer the following key questions: (1) Do HRV parameters significantly differ between active and sham treatment groups? (2) Are changes in HRV more pronounced when evaluating pre-post effects within the active treatment arm, thereby capturing the full effect of the intervention—including any placebo component? (3) Are specific types of interventions—such as neuromodulation, pharmacologic, or physical therapies—more likely to impact HRV? Finally, (4) are these autonomic effects moderated by markers of systemic inflammation, such as body mass index (BMI), which could serve as surrogates for inflammatory load and individual susceptibility to autonomic dysregulation?

2. Methods

2.1. Protocol and Registration

This systematic review was conducted according to the PRISMA statement (Preferred Reporting Items for Systematic Review and Meta-Analyses) [20]. The protocol was registered a priori in PROSPERO (registration number CRD42023448264).

2.2. Search Strategy

The searches were conducted in PubMed, CENTRAL, and Embase databases in July 2023 and updated in March 2025. The main keywords for the search strategy were “AUTONOMIC NERVOUS SYSTEM” OR “ANS” OR “HEART RATE VARIABILITY” OR “HRV” AND “PAIN”. The search strategy was built by combining free-text and MeSH terms and combined using the Boolean operator OR. Between the components AND was used as Boolean operator. No additional search filters were used in this study. We also screened the reference lists of all relevant publications for additional papers (backward reference search).

2.3. Eligibility Criteria

All randomized controlled clinical trials (RCTs) reporting any HRV parameters as an outcome on patients older than 18 years with any pain condition (i.e., acute or chronic) published in English were included. Non-RCTs, observational studies, reviews, commentaries, and editorials were excluded.

2.4. Data Collection and Extraction

Search results were screened using Covidence online software tool (Melbourne, Veritas Health Innovation, Australia). After removing duplicate articles, the title and abstract were screened by four separate reviewers independently (MD, GV, MA, and BA). Subsequently, two reviewers performed the full-text screening independently from each other (MD and GV). Discrepancies were discussed after each stage, and consensus was achieved by an independent reviewer (MA). Finally, 23 studies were included in the review, reported in Figure 1.
Data extraction was performed in an Excel template, with the following items: (1) bibliometric variables, author(s), year of publication, and country; (2) population variables, sample size, sex, age, BMI, and diagnosis; (3) intervention; and (4) outcome measures. The participant and intervention characteristics of the included RCTs are reported in Table 1.

2.4.1. Intervention Measure

A meta-analysis was performed, grouping studies with similar association variables and comparable intervention measures. In this context, (a) pain duration (i.e., acute or chronic), and (b) pain intensity at baseline and post-intervention were considered to perform sub-group analysis. The final decision was made post hoc, although the plan to pool comparable data was outlined ‘a priori’ in our study protocol.

2.4.2. Outcome Measures

Following the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology in 1996 [21], we aimed to explore the changes in HRV metrics in patients with pain following different types of intervention.
HRV variables consist of time-domain and frequency-domain indices. The time-domain variables extracted were (a) Root Mean Square of Successive R-R interval Differences (RMSSD), (b) Standard Deviation of N-N intervals (SDNN), and (c) percentage of successive normal-to-normal intervals which exceed 50 ms from the previous one (pNN50). Frequency-domain components considered were (a) low-frequency (LF), (b) high-frequency (HF), and (c) low-frequency/high-frequency ratio. The LF component reflects heart rate variations within a frequency span of 0.04 to 0.15 Hz, often interpreted as predominantly reflecting sympathetic nervous system activity. On the other hand, the HF component, spanning a frequency range from 0.15 to 0.40 Hz, is considered an indicator of parasympathetic nervous system influence. The ratio of these two parameters describes the sympathovagal balance [22,23].
Table 1. Participant and intervention characteristics of the included RCT studies (n = 23).
Table 1. Participant and intervention characteristics of the included RCT studies (n = 23).
Author, YearCountryN Subjects (Interv/Total)Study DesignAge *Female (%)BMI (kg/m2) *DiagnosisInterventionComparatorType of Pain (Duration)Pain ScaleAssessment Period
Bácker M, 2008 [24]Germany9/19Parallel43.5 ± 8.515 (88.2)n/rMigraineAcupunctureShamAcutePDIBaseline and 12 weeks
Berry M, 2014 § [25]USA8/14Pilot41.8 ± 6.61 (12)n/rChronic painBiofeedbackNo interventionChronicBPIBaseline and 4 weeks
Buttagat V, 2021 [26] Thailand18/36Parallel22.9 ± 3.411 (61.11)20.3Back pain associated with myofascial pain syndromeThai massageNo interventionChronicVASBaseline and immediate post-intervention
Cerritelli F, 2021 [27]Italy15/30Parallel42.3 ± 7.37 (40)24.1 ± 3.5Low back painOsteopathic manipulative treatmentShamChronicVASBaseline, immediate (T1), and sustained response (T2), 4 wks.
Espejo-Antúnez L, 2021 [28]Switzerland25/49Parallel37.0 ± 6.60 (0)25.21 ± 2.8Low back painInterferential current therapyShamChronicNPRSBaseline and Immediate effect
Fernández-Morales C, 2024 § [29]Spain14/31Pilot24.0 ± 5.5n/r24.0 ± 1.4Flight-related neck painPhysical therapyNo interventionAcuteNPRSBaseline and 4 wks, 8 sessions
Hu X, 2021 [30]China50/100Parallel50.8 ± 15.3 28 (56)24.8 ± 2.4Chronic non-specific low back pain (CNSLBP)Silver needle therapyNo interventionChronicNRSBaseline, 1, 2, 3, and 6 months
Jiang W, 2011 [31]USA20/40Parallel55.1 ± 14.49 (45)31.00 ± 8.1Painful diabetic neuropathyPregabalinPlaceboChronicVASBaseline and 4 wks
Liao CD, 2017 [32]Taiwan22/44Parallel50.78 ± 8.0817 (77.3)21.96 ± 2.31Neuropathic painStellate ganglion irradiationShamChronicVASBaseline and 6 wks, 12 sessions
Matsubara T, 2011 * [33]Japan11/33Parallel35.5 ± 6.411 (100%)n/rNeck Pain Local acupunctureNo interventionChronicVRSBaseline and Immediate effect
Matsubara T, 2011 * [33]Japan11/33Parallel37.2 ± 7.011 (100%)n/rNeck PainDistal acupunctureNo interventionChronicVRSBaseline and Immediate effect
Moreira RM, 2023 § [34]Brazil9/18Pilot58 ± 9.899 (100)28.27 ± 3.54FibromyalgiaAcupunctureNo interventionChronicNPRSBaseline and 6 wks, 6 sessions
Olliges E, 2024 * [35]Germany21/60Parallel64.19 ± 9.312 (42.8)n/rKnee OsteoarthritisOLP-painNo interventionChronicNRSBaseline and 3 weeks
Olliges E, 2024 * [35]Germany20/60Parallel66.8 ± 9.79 (45)n/rKnee OsteoarthritisOLP-moodNo interventionChronicNRSBaseline and 3 weeks
Paccione CE, 2022 * [36]France28/116Parallel48.25 ± 8.8827 (96.4)29.26 ± 6.49FibromyalgiaActive tVNSShamChronicNRSBaseline and 2 weeks
Paccione CE, 2022 * [36]France29/116Parallel48.25 ± 8.8827 (96.4)29.26 ± 6.49FibromyalgiaActive MDBShamChronicNRSBaseline and 2 weeks
Patel ABU, 2023 [37]UK43/86Parallel47 (33 to 54)17 (39.6)n/rPerioperative pain after orthopedic surgeryTranscutaneous auricular nerve stimulationShamAcuteVASBaseline (before surgery) and After surgery
Paz T, 2023 * [38]Brazil20/58Parallel40 ± 1715 (75)n/rMusculoskeletal painSpinal ManipulationPlaceboChronicNRSBaseline and Immediate effect
Paz T, 2023 * [38]Brazil19/58Parallel48 ± 1113 (68)n/rMusculoskeletal painMyofascial ManipulationPlaceboChronicNRSBaseline and Immediate effect
Prim JH, 2019 †,§ [39]USA20/20Pilot-Crossover43 ± 13.3712 (60)25.94 ± 4.46CLBPTranscranial alternating current stimulationPlaceboChronicDVPRSBaseline and Immediate effect
Toro-Velasco C, 2009 † [40]Spain11/11Crossover51 ± 158 (60)n/rCTTHHead-neck massagePlaceboChronicNPRSBaseline, Immediate and after 24 h, 2 sessions
Warth M, 2015 [41]Germany42/42Parallel63.8 ± 14.128 (66.7)n/rPalliative care painMusic therapyNo interventionAcuteVASBaseline and at 48 h after 2 sessions.
Wong A, 2017 [42]Korea17/31Parallel51 ± 218 (100)23.1 ± 0.5FibromyalgiaTai ChiNo interventionChronicVASBaseline and 12 weeks
Xuan J, 2022 [43]China21/42Parallel51.14 ± 8.66 7 (33.3)25.07 ± 3.77Pancreatitis painTranscutaneous electrical acustimulationShamAcuteVASBaseline and 48 h
Yamamoto K, 2011 § [44]Japan9/18Pilotn/r3 (33)n/rIncurable stomach cancer during palliative care for painWW-Footbath MethodNo interventionChronicVASBaseline and Immediate effect
Yeh ML, 2018 [45]Taiwan39/80Parallel55.64 ± 14.8324 (61.5)n/rPost-operative pain in hemorrhoidectomyTranscutaneous acupoint electrical stimulationNo interventionAcuteVASBaseline and after 4 sessions
Younes M, 2017 [46]France10/17Parallel31 ± 90 (0)23.67 ± 4.34Low back painSpinal manipulative treatmentShamAcuteNPRSBaseline and immediate effect, 2 sessions
* Matsubara T, 2011; Paccione CE, 2022; Paz T, 2023 and Olliges E, 2024 are parallel RCTs with three or more groups. † Toro-Velasco C, 2009; Prim JH, 2019; are crossover RCTs. § Yamamoto K, 2011; Prim JH, 2019; Moreira RM, 2023; Fernández-Morales C, 2024 and Berry, 2014 are pilot RCTs. * Bácker, 2008 demographics include entire sample values. * Age and BMI are reported as mean + SD, except for Patel ABU, 2023, which is in range. Pain was assessed in all articles at baseline. Abbreviations: BPI: Brief Pain Inventory; CLBP: Chronic low back pain; CTTH: Chronic tension-type headache; DVPRS: Defense and Veterans Pain Rating Scale; MDB: Meditative-Based Diaphragmatic Breathing; NPRS: Numerical Pain Rating Scale; NRS: Numeric Rating Scale; PDI: Pain Disability Index; tVNS: Transcutaneous Vagus Nerve Stimulation; VAS: Visual Analog Scale; VRS: Verbal rating scale.
All parameters were extracted at baseline and post-intervention. Studies are reported with the metrics that were presented in the original studies, meaning no calculations were performed to obtain standardized metrics. The recording methods for HRV measurements were also registered for further discussion (device, instructions, preparation, position, collection time, analysis software, and analysis time).

2.5. Risk of Bias Assessment

The risk of bias was evaluated using the Cochrane Collaboration’s tool (Risk of Bias Tool 2.0). Included RCTs were analyzed across five domains: the randomization process (D1), deviations from the intended intervention (D2), missing outcome data (D3), outcome measurement (D4), and selective reporting of results (D5). Each domain was graded as low risk (low), moderate risk (some concerns), or high risk (high) by two assessors independently (MA and NA). A final score of bias was determined after the five domains were evaluated. Discrepancies were resolved through consensus. Studies were not excluded based on the results of the risk of bias assessment to avoid the selective reporting of study findings.

2.6. Heterogeneity Assessment

Heterogeneity among studies was assessed using Cochran’s Q test and the I2 statistic. A p-value < 0.05 on Cochran’s Q test was considered statistically significant heterogeneity, while I2 values were interpreted as follows: <30% as low, 30–50% as moderate, and >50% as high heterogeneity. Publication bias was examined using funnel plots and Egger’s test, with a p-value < 0.05 suggesting small publication bias.

2.7. Data Analysis

The effect size was calculated as the mean difference of HRV metrics between the intervention and control groups at baseline versus post-intervention, using Hedges’ g statistic. The statistical difference in the pooled effect size was computed using a 95% confidence interval (CI). A random-effects model was adapted to account for anticipated heterogeneity, providing a more conservative estimate of the effect size than the fixed model. In addition to between-group comparisons, we conducted one-group meta-analyses using pre- and post-intervention data from the active treatment arms to capture the total effect of interventions. For each included study, we extracted the means and standard deviations of HRV parameters before and after intervention. This analytic strategy allowed us to assess the full magnitude of autonomic modulation following pain interventions. Meta-regression analyses were conducted to explore the moderating effect of BMI on the association between interventions and HRV outcomes. Separate mixed-effects meta-regression models were fitted for each HRV parameter, with BMI included as a continuous moderator. Regression coefficients, standard errors, and p-values were calculated, along with the proportion of explained variance (R2) and residual heterogeneity (I2). In addition to assessing BMI as a main effect, moderator analyses were performed using the Q-model test to determine whether BMI significantly modified intervention effects. Data were synthesized and analyzed using the software R Statistical Software (version 4.2.3; R Core Team, 2024).

3. Results

3.1. Study Selection

Twenty-three RCTs were included with a total sample size of 1262 subjects [24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46]. The mean age of the overall population was 44.3 years (SD = 10.4). Women comprised 52.6% of the study subjects. Diagnoses treated in the trials included migraine, chronic neck pain, low back pain, fibromyalgia, osteoarthritis, perioperative pain, post-operative hemorrhoidectomy, acute mechanical back pain, neuropathic pain (including complex regional pain syndrome type 1, postherpetic neuralgia, and post-surgery pain), painful diabetic neuropathy, chronic non-specific low back pain, acute pancreatitis, musculoskeletal pain, palliative pain, chronic unspecified pain, chronic tension-type headache, and back pain associated with myofascial pain syndrome. HRV was evaluated in all studies both at baseline and after pain interventions, with a recording session duration that had a median of 5 min (IQR: 5 to 10 min), ranging from 2 to 60 min. Table 2 describes HRV variables and recording methods considered in the included studies.

3.2. Time-Domain Measures

The meta-analyses of HRV parameters revealed varying effects across the three time-domain measures (Figure 2). For RMSSD (13 studies, n = 521), the analysis showed a small-to-moderate positive effect (Hedges’ g = 0.33, 95% CI [−0.077, 0.730], p = 0.11) with substantial heterogeneity (I2 = 79.5%). SDNN analysis (7 studies, n = 311) demonstrated a moderate positive effect approaching significance (Hedges’ g = 0.44, 95% CI [−0.04, 0.91], p = 0.07) with considerable heterogeneity (I2 = 71.2%). The pNN50 analysis (5 studies, n = 193) yielded the smallest effect size (Hedges’ g = 0.22, 95% CI [−0.51, 0.95], p = 0.55) with substantial heterogeneity (I2 = 82.2%). While all three parameters showed positive effect sizes, none reached statistical significance at the conventional p < 0.05 level, with SDNN showing the strongest trend toward significance. All parameters demonstrated substantial between-study heterogeneity (I2 > 70%), suggesting considerable variation in intervention effects across studies.

3.3. Frequency-Domain Measures

Based on the meta-analyses of frequency-domain HRV parameters, differential effects were observed across the three measures (Figure 3). LF power showed a small positive effect (Hedges’ g = 0.14, 95% CI [−0.03, 0.31], p = 0.09) with notably consistent findings across studies (I2 = 0%). HF power demonstrated the smallest effect size (Hedges’ g = 0.07, 95% CI [−0.26, 0.39], p = 0.68) but exhibited substantial heterogeneity between studies (I2 = 73.4%). Most notably, the LF/HF ratio showed a significant moderate negative effect (Hedges’ g = −0.33, 95% CI [−0.62, −0.004], p = 0.03) with moderate heterogeneity (I2 = 48.2%). This significant reduction in the LF/HF ratio, combined with the non-significant trends in individual LF and HF components, suggests a potential shift in autonomic balance following intervention, possibly indicating a relative increase in parasympathetic modulation compared to sympathetic activity. The analysis encompassed a substantial dataset (LF: 15 studies, n = 603; HF: 19 studies, n = 821; LF/HF: 14 studies, n = 520), providing robust evidence, particularly for the LF/HF ratio findings, while the heterogeneity in HF power suggests variable intervention effects across different study contexts.

3.4. One-Group Meta-Analysis (The Combined Effect of the Intervention)

The one-group meta-analysis of HRV parameters revealed significant improvements in vagal and overall HRV following intervention. RMSSD demonstrated a large and statistically significant pooled effect size (Hedges’ g = 1.084, p < 0.001), indicating robust increases in vagal-mediated HRV. HF power also showed a significant positive effect (g = 0.622, p < 0.001), further supporting enhanced parasympathetic activity. SDNN exhibited a moderate, significant effect (g = 0.455, p = 0.004), while pNN50 (g = 0.186, p = 0.469) and LF (g = 0.099, p = 0.447) showed small, non-significant changes. The LF/HF ratio did not show a significant change (g = −0.087, p = 0.581). Heterogeneity was high across most parameters, suggesting substantial variability between studies.

3.5. Meta-Regression

The meta-regression analyses revealed differential effects of BMI on HRV parameters, with the strongest relationships observed for HF power and RMSSD. As a main effect, BMI demonstrated a negative association with HF power (coefficient = −0.172, SE = 0.088, p = 0.108), explaining 43.3% of the variance in effect sizes (R2 = 0.433), indicating that individuals with higher BMI showed reduced HF power responses. Similarly, RMSSD showed a negative relationship with BMI (coefficient = −0.158, SE = 0.187, p = 0.445, R2 = 0.152), suggesting that higher BMI values were associated with reduced vagally mediated HRV responses. When tested as a moderator, BMI significantly modulated the intervention effects on HF power (Q_model = 22.286, p < 0.001) and RMSSD (Q_model = 7.220, p = 0.007), with substantial residual heterogeneity (I2 = 82.89% and 90.09%, respectively). The LF/HF ratio showed a weak positive moderation effect (coefficient = 0.047, SE = 0.069, p = 0.541, R2 = 0.136), suggesting slightly higher sympathovagal balance in those with elevated BMI, while LF power demonstrated a slight negative association (coefficient = −0.047, SE = 0.055, p = 0.458, R2 = 0.194).
These findings suggest that BMI plays a more pronounced role in modulating vagal-related HRV parameters (HF power and RMSSD) compared to sympathetically mediated or balanced measures (LF power and LF/HF ratio), with higher BMI generally associated with attenuated HRV responses to interventions. Specifically, as BMI increases, there is a consistent pattern of reduced vagal tone (lower HF power and RMSSD) and a slight shift toward sympathetic predominance (higher LF/HF ratio), suggesting that elevated BMI may compromise autonomic regulation, particularly parasympathetic function (Table 3).

3.6. Individual Results: Most Effective Interventions to Modulate HRV

The analysis identified the three most effective interventions for improving RMSSD, a key marker of vagal-mediated HRV, from the dataset. The top intervention was reported by Toro-Velasco et al. (2009), which demonstrated a substantial RMSSD increase of 66.10, indicating a robust enhancement in parasympathetic activity [40]. The second most effective intervention was from Fernández-Morales et al. (2024), with an RMSSD improvement of 31.10 [29], and the third was from Espejo-Antúnez et al. (2021), showing an RMSSD increase of 21.34 [28]. These findings highlight that those interventions in these studies produced the largest gains in vagal tone, as measured by RMSSD.

3.7. Risk of Bias

Out of the 23 included articles, 14 (60.7%) were rated as having a low risk of bias, 8 (34.8%) raised some concerns, and 1 (4.3%) demonstrated a high risk of bias (Figure 4).

4. Discussion

In the present meta-analysis of 23 RCTs including 1262 patients, we explored the relationship between changes in HRV metrics with pain treatment. This meta-analysis provides a robust synthesis of randomized controlled trials to evaluate the effect of pain interventions on HRV parameters. Grounded in our original objectives, we structure this discussion by systematically addressing each of the four predefined research questions.

4.1. Do HRV Parameters Significantly Differ Between Active and Sham Treatment Groups?

Our findings support a significant improvement in autonomic balance following active pain-modulating interventions, as evidenced by a significant reduction in the LF/HF ratio. While HF power showed a small and non-significant increase across studies, the consistent decrease in LF/HF suggests a relative shift toward enhanced parasympathetic modulation. These shifts show enhanced parasympathetic tone and reduced sympathetic dominance following pain relief, consistent with the restoration of autonomic balance previously documented in pain resolution studies [5,16,17]. The increase in HF power aligns with Porges’ polyvagal theory, which postulates that improved parasympathetic function is associated with reduced threat perception and enhanced regulation of nociceptive signals [47,48].
The lack of significant changes in RMSSD, pNN50, and LF suggests that certain HRV parameters may be more sensitive to detecting autonomic responses to pain interventions than others. A differential responsiveness of HRV metrics has been previously observed by Shaffer and Ginsberg (2017), who demonstrated that frequency-domain measures may better reflect specific autonomic modulation compared to time-domain indices in certain physiological contexts [23]. Additionally, substantial heterogeneity across studies (I2 ranging from 44.9% to 82.5%) underscores the variability in study designs, patient populations, intervention modalities, and HRV measurement protocols—all factors known to influence autonomic assessment reliability [49,50].
These findings support the growing recognition that pain and autonomic regulation share overlapping central neural networks, particularly in the anterior cingulate cortex, insula, and periaqueductal gray matter [51,52], regions that simultaneously influence both nociceptive processing and cardiac vagal control.

4.2. Are HRV Changes More Pronounced Within the Active Group (Pre- vs. Post-Intervention), Reflecting Both Specific and Non-Specific (Placebo) Effects?

One-group meta-analyses revealed substantially larger effect sizes compared to between-group analyses, with RMSSD showing a large, statistically significant pooled effect and HF power demonstrating a moderate-to-large effect. These robust enhancements in parasympathetic activity post-intervention, regardless of comparator groups, suggest that both specific treatment mechanisms and non-specific factors (e.g., placebo responses, patient expectations, therapeutic alliance) contribute synergistically to autonomic regulation [53,54].
Importantly, SDNN—a measure of overall HRV reflecting total autonomic modulation—also improved significantly (g = 0.455, 95% CI: 0.150 to 0.760; p = 0.004), indicating that pain interventions enhance global cardiac regulatory capacity. The LF/HF ratio, however, did not show significant within-group change (g = −0.087, p = 0.581), consistent with recent critiques of this metric as an imprecise indicator of sympathovagal balance [55,56]. The inconsistency in LF/HF findings may reflect methodological heterogeneity in measurement protocols, which have been shown to significantly influence frequency-domain analyses [49,50].
These findings highlight the value of within-group analyses in pain research, which capture the cumulative neurophysiological effects of therapeutic context—including expectancy, conditioning, and social learning components that activate endogenous pain modulation systems [57,58]. The larger effect sizes in within-group analyses also align with research demonstrating that contextual factors can amplify autonomic responses through central mechanisms involving prefrontal-brainstem pathways that simultaneously influence both pain perception and cardiovascular regulation [5,51]. This suggests that optimal pain management approaches should deliberately leverage both specific and non-specific mechanisms to maximize autonomic recovery.

4.3. Are Specific Types of Interventions More Likely to Modulate HRV?

Among the diverse interventions analyzed, three categories emerged as particularly effective in modulating HRV parameters: manual therapy, electrostimulation, and neuromodulation approaches. Manual therapies (e.g., massage, osteopathic manipulation, and physical therapy) demonstrated consistent improvements in parasympathetic indices, likely through mechanoreceptor stimulation and subsequent activation of periaqueductal gray matter and descending inhibitory pathways [27,59]. Electrostimulation interventions (e.g., interferential therapy, transcutaneous electrical nerve stimulation) showed moderate-to-large effects on RMSSD and HF power, potentially operating through counter-irritation mechanisms and segmental inhibition of nociceptive transmission [60,61]. Neuromodulation techniques (e.g., transcranial direct current stimulation, vagus nerve stimulation) exhibited significant effects on frequency-domain parameters, directly targeting central autonomic networks [24,62,63].
Quantitative analysis identified the three most effective interventions for increasing RMSSD as massage therapy from Toro-Velasco et al. (2009) with a mean increase of 66.10 ms; physical therapy with HRV biofeedback from Fernández-Morales et al. (2024) with an increase of 31.10 ms; and interferential current therapy from Espejo-Antúnez et al. (2021) with an increase of 21.34 ms. These three approaches share a common feature—they provide strong afferent input to the central nervous system, either through mechanoreceptor activation or electrical stimulation, potentially restoring descending inhibitory control over both nociceptive and autonomic circuits [64,65].
The superior efficacy of these interventions may involve several mechanisms: (1) direct stimulation of mechanoreceptors that synapse with inhibitory interneurons in the dorsal horn [60]; (2) activation of descending serotonergic and noradrenergic pathways from the brainstem, modulating both pain and autonomic function [66]; and (3) reduction in central sensitization through normalization of anterior cingulate cortex and insular activity [27,67]. However, the wide heterogeneity in study designs, treatment protocols, and HRV measurement techniques (I2 > 70% for most parameters) limits direct comparisons between intervention types. Future head-to-head randomized trials with standardized HRV measurement protocols are warranted to confirm the superiority of specific approaches and elucidate their mechanistic pathways.

4.4. Are the Observed HRV Changes Moderated by Markers Such as BMI, Which Could Reflect Systemic Inflammation?

Meta-regression analysis identified BMI as a significant moderator of HRV responses to pain interventions, with pronounced effects on both high-frequency power and LF/HF ratio. These significant moderation effects indicate that patients with higher BMI demonstrated both attenuated improvements in parasympathetic function (lower HF power) and greater sympathetic predominance (higher LF/HF ratio) following treatment. This inverse relationship between BMI and vagal reactivity aligns with established literature linking adiposity to autonomic dysregulation through multiple pathophysiological mechanisms [68,69,70].
The moderation effect of BMI likely reflects underlying metabolic-inflammatory processes relevant to both pain processing and autonomic regulation. Adipose tissue functions as an active endocrine organ, secreting pro-inflammatory cytokines (IL-6, TNF-α) and adipokines that contribute to systemic low-grade inflammation [71,72]. This inflammatory state has been shown to impair vagal function through multiple pathways: direct cytokine effects on brainstem autonomic nuclei [73]; disruption of blood-brain barrier integrity affecting central autonomic networks [74]; and reduced sensitivity of peripheral baroreceptors critical for cardiovascular control [75,76]. We have also shown that increased BMI leads to an attenuation of inhibitory pain processes in osteoarthritis pain [77].
Our findings have important clinical implications, suggesting that patients with elevated BMI may represent a distinct phenotype requiring targeted therapeutic approaches to restore autonomic balance. Previous research has demonstrated that weight reduction interventions improve HRV parameters [69,78], suggesting that metabolic improvements may complement direct pain management. The significant moderation effect of BMI also provides indirect support for the “inflammatory reflex” concept proposed by Tracey (2002), wherein vagus nerve activity regulates systemic inflammation through cholinergic anti-inflammatory pathways [79].
These results underscore the importance of considering individual inflammatory burden when interpreting HRV responses to pain interventions and potentially tailoring treatments based on metabolic profiles. Future clinical trials should explore whether combined interventions targeting both nociceptive pathways and metabolic health (e.g., anti-inflammatory diets, exercise, or pharmacological approaches) yield synergistic benefits in restoring autonomic regulation in patients with pain and elevated BMI [80,81,82].
While our analysis included both acute and chronic pain conditions, the proposed model of autonomic dysregulation as compensatory failure is likely more applicable to chronic pain, where prolonged nociceptive input, central neuroplasticity, and inflammation impact vagal tone. In contrast, autonomic changes in acute pain are typically transient and driven by sympathetic activation. Additionally, the three most effective interventions for improving RMSSD were all conducted in Spanish populations, raising the possibility that cultural or contextual factors—such as therapeutic expectations or clinician-patient dynamics—may have influenced outcomes. These observations underscore the need for future studies to stratify analyses by pain chronicity and explore cultural moderators of autonomic response. Finally, although women comprised over half of the pooled study population, most included RCTs did not report HRV outcomes disaggregated by sex. As such, we were unable to evaluate sex as a potential moderator. Given well-documented sex differences in baseline vagal tone and HRV reactivity, future trials should report sex-specific data to improve the generalizability and mechanistic understanding of autonomic modulation in pain interventions.

5. Conclusions

The markedly stronger effects observed in the one-group meta-analyses—particularly in RMSSD and HF—compared to the smaller and often non-significant differences in between-group comparisons, suggest that HRV captures broader physiological shifts beyond the immediate contrast between active and sham interventions. Furthermore, the significant moderating role of BMI on vagally mediated HRV indices (HF and RMSSD) supports the idea that systemic inflammation and metabolic dysfunction influence both pain processing and autonomic regulation.
These findings reinforce the hypothesis that pain and autonomic dysfunction are not strictly causally linked but may instead represent parallel expressions of a shared deregulatory process. In this context, HRV may serve as a critical physiological window into compensatory failure in pain control—a concept aligned with emerging evidence on the compensatory pain system [83]. Rather than reflecting a unidirectional recovery from pain, HRV may help detect individuals whose autonomic and neuroimmune systems are unable to adapt effectively to persistent nociceptive input.
This synthesis highlights HRV’s potential as a clinically actionable biomarker for stratifying patients and tailoring mechanism-based treatments. Future studies should employ standardized HRV protocols and integrate inflammatory and neuroimaging markers to deepen mechanistic understanding and guide personalized interventions at the autonomic-nociceptive interface.

Author Contributions

M.D., B.A., M.F.A. and G.d.P.V. contributed to the literature search and data extraction. M.D., B.A. and F.F. performed all statistical analyses. M.D., B.A., A.G., N.A., P.G., C.B.d.M.M. and D.S. contributed to drafting the first version of the manuscript. F.F. led the conceptualization and supervision of the project. All authors have read and agreed to the published version of the manuscript.

Funding

FF is supported by an NIH R01 grant (1R01HD082302-01A1). DS is supported by a VA Rehabilitation Research and Development I01 grant (1I01RX003769-01).

Institutional Review Board Statement

Not applicable. Meta-analysis of published data.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data synthesized in this study are from published trials.

Conflicts of Interest

The authors declare no known conflicts of interest associated with this publication.

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Figure 1. PRISMA flow of study selection.
Figure 1. PRISMA flow of study selection.
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Figure 2. Forest plot for RMSSD, SDNN, and pNN50 comparing experimental and control groups. The plots show standardized mean differences (SMDs) with 95% confidence intervals. Included studies for SDNN: Espejo-Antúnez et al. [28], Fernández-Morales et al. [29], Jiang et al. [31], Liao et al. [32], Moreira et al. [34], Paccione et al. [36], Patel et al. [37], Paz et al. [38], Prim et al. [39], Toro-Velasco et al. [40], Younes et al. [46]. Included studies for pNN50: Jiang et al. [31], Liao et al. [32], Moreira et al. [34], Patel et al. [37], Prim et al. [39], Toro-Velasco et al. [40], Yeh et al. [45]. Included studies for RMSSD: Fernández-Morales et al. [29], Jiang et al. [31], Moreira et al. [34], Paccione et al. [36].
Figure 2. Forest plot for RMSSD, SDNN, and pNN50 comparing experimental and control groups. The plots show standardized mean differences (SMDs) with 95% confidence intervals. Included studies for SDNN: Espejo-Antúnez et al. [28], Fernández-Morales et al. [29], Jiang et al. [31], Liao et al. [32], Moreira et al. [34], Paccione et al. [36], Patel et al. [37], Paz et al. [38], Prim et al. [39], Toro-Velasco et al. [40], Younes et al. [46]. Included studies for pNN50: Jiang et al. [31], Liao et al. [32], Moreira et al. [34], Patel et al. [37], Prim et al. [39], Toro-Velasco et al. [40], Yeh et al. [45]. Included studies for RMSSD: Fernández-Morales et al. [29], Jiang et al. [31], Moreira et al. [34], Paccione et al. [36].
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Figure 3. Forest plot for HF, LF, and LF/HF ratio comparing experimental and control groups. The plots show standardized mean differences (SMDs) with 95% confidence intervals. Included studies for LF: Buttagat et al. [26], Fernández-Morales et al. [29], Hu et al. [30], Jiang et al. [31], Matsubara et al. [33], Moreira et al. [34], Paccione et al. [36], Patel et al. [37], Paz et al. [38], Prim et al. [39], Toro-Velasco et al. [40], Warth et al. [41], Wong et al. [42], Yeh et al. [45], Younes et al. [46]. Included studies for LF/HF ratio: Buttagat et al. [26], Fernández-Morales et al. [29], Hu et al. [30], Jiang et al. [31], Matsubara et al. [33], Moreira et al. [34], Paccione et al. [36], Patel et al. [37], Paz et al. [38], Prim et al. [39], Toro-Velasco et al. [40], Yeh et al. [45], Younes et al. [46]. Included studies for HF: Buttagat et al. [26], Fernández-Morales et al. [29], Hu et al. [30], Jiang et al. [31], Matsubara et al. [33], Moreira et al. [34], Paccione et al. [36], Toro-Velasco et al. [40], Yeh et al. [45], Younes et al. [46].
Figure 3. Forest plot for HF, LF, and LF/HF ratio comparing experimental and control groups. The plots show standardized mean differences (SMDs) with 95% confidence intervals. Included studies for LF: Buttagat et al. [26], Fernández-Morales et al. [29], Hu et al. [30], Jiang et al. [31], Matsubara et al. [33], Moreira et al. [34], Paccione et al. [36], Patel et al. [37], Paz et al. [38], Prim et al. [39], Toro-Velasco et al. [40], Warth et al. [41], Wong et al. [42], Yeh et al. [45], Younes et al. [46]. Included studies for LF/HF ratio: Buttagat et al. [26], Fernández-Morales et al. [29], Hu et al. [30], Jiang et al. [31], Matsubara et al. [33], Moreira et al. [34], Paccione et al. [36], Patel et al. [37], Paz et al. [38], Prim et al. [39], Toro-Velasco et al. [40], Yeh et al. [45], Younes et al. [46]. Included studies for HF: Buttagat et al. [26], Fernández-Morales et al. [29], Hu et al. [30], Jiang et al. [31], Matsubara et al. [33], Moreira et al. [34], Paccione et al. [36], Toro-Velasco et al. [40], Yeh et al. [45], Younes et al. [46].
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Figure 4. Risk of bias in individual studies across five domains (D1–D5), assessed using the Cochrane Risk of Bias 2.0 tool. Included studies: Bäcker et al. [24], Berry et al. [25], Buttagat et al. [26], Cerritelli et al. [27], Espejo-Antúnez et al. [28], Fernández-Morales et al. [29], Hu et al. [30], Jiang et al. [31], Liao et al. [32], Matsubara et al. [33], Moreira et al. [34], Olliges et al. [35], Paccione et al. [36], Patel et al. [37], Paz et al. [38], Prim et al. [39], Toro-Velasco et al. [40], Warth et al. [41], Wong et al. [42], Xuan et al. [43], Yamamoto et al. [44], Yeh et al. [45], Younes et al. [46].
Figure 4. Risk of bias in individual studies across five domains (D1–D5), assessed using the Cochrane Risk of Bias 2.0 tool. Included studies: Bäcker et al. [24], Berry et al. [25], Buttagat et al. [26], Cerritelli et al. [27], Espejo-Antúnez et al. [28], Fernández-Morales et al. [29], Hu et al. [30], Jiang et al. [31], Liao et al. [32], Matsubara et al. [33], Moreira et al. [34], Olliges et al. [35], Paccione et al. [36], Patel et al. [37], Paz et al. [38], Prim et al. [39], Toro-Velasco et al. [40], Warth et al. [41], Wong et al. [42], Xuan et al. [43], Yamamoto et al. [44], Yeh et al. [45], Younes et al. [46].
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Table 2. HRV variables and recording methods considered in the included studies (n = 23).
Table 2. HRV variables and recording methods considered in the included studies (n = 23).
IDAuthor, YearHRV Parameters and MetricsPositionRecording TimeIBI ExtractionSoftware for Analysis
1Bácker M, 2008 [24]LF (s2), HF (s/L)At rest2 minECGMatlab (Mathworks, Natick, MA, USA)
2Berry M, 2014 [25]HRV Coherence Ratio (Hz)At rest10 minn/rn/r
3Buttagat V, 2021 [26] LF (ms2), HF (ms2), LF/HFn/r10 minECGn/r
4Cerritelli F, 2021 [27]LF (nu), HF (nu), LF/HFn/r5 minPulse oximeterKubios software
5Espejo-Antúnez L, 2021 [28]RMSSDAt rest and during intervention30 minFirstbeat Bodyguard® monitorKubios® HRV software (v.2.1.)
6Fernández-Morales C, 2024 [29]LF (ms2), HF (ms2), LF/HF, RMSSD, pNN50Prone at rest15 minFirstbeat Bodyguard® monitorKubios software
7Hu X, 2021 [30]LF (ln), HF (ln), LF/HFn/rn/rZSY-1 Heart Rate Variation Detector, Wegene Technology Inc. (Shenyang, China)n/r
8Jiang W, 2011 [31]RMSSD, SDNN, pNN50, LF (nu), HF (nu), LF/HFSupine at rest60 minECGVivo-VMLA-036-00 3 Logic software
9Liao CD, 2017 [32]RMSSD, SDNN, LF (ms2), HF (ms2), LF/HFSupine at rest10 minANSWatchVR wrist monitorANSWatchVR Manager Pro (version 1.94) software.
10Matsubara T, 2011 [33]LF (ms2), HF (ms2), LF/HFSupine at rest5 minECGHRV analysis software (TARAWA/WIN)
11Moreira RM, 2023 [34]RMSSD, SDNN, pNN50, LF (ms2), HF (ms2), LF/HFSupine at rest5 minPolarR RS800CXKubios HRV Standard software
12Olliges E, 2024 [35]HF (%)n/r5 minECGKubios HRV software version 2.2
13Paccione CE, 2022 [36]RMSSD, pNN50, HF (ms2)Seated at rest3 minPolar H7n/r
14Patel ABU, 2023 [37]RMSSD, SDNN, LF (ms2), HF (ms2)At rest10 minECGn/r
15Paz T, 2023 [38]RMSSD, LF (ms2), HF (ms2), LF/HFSupine at rest10 minPolar RS800cxKubios software, version 2.2
16Prim JH, 2019 † [39]RMSSD, SDNN, LF (ms2), HF (ms2), LF/HFSeated at rest2 minECGMATLAB and CardioBatch software
17Toro-Velasco C, 2009 † [40]RMSSD, SDNN, LF, HFSupine at rest5 minECGNH300 Software
18Warth M, 2015 [41]HF (ms2)n/r5 minn/rKubios software version 2.1
19Wong A, 2017 [42]LF (ms2), HF (ms2), LF/HFSupine at rest10 minPolar device SA-2000E modeln/r
20Xuan J, 2022 [43]LF, HF, LF/HF (metrics n/r)Supine at rest30 minECGn/r
21Yamamoto K, 2011 [44]HF (ms/Hz), LF/HFRecumbent positionn/rECGMicrosoft Excel (2003)
22Yeh ML, 2018 [45]LF, HF, LF/HF (metrics n/r)Sitting position5 minHRVm, 8Z11n/r
23Younes M, 2017 [46]RMSSD, LF (ms2), HF (ms2), LF/HFSupine at rest35 minECGSCILAB (INRIA, France).
† Toro-Velasco C, 2009; Abbreviations: RMSSD: Root Mean Square of Successive RR interval Differences; SDNN: Standard Deviation of NN intervals; pNN50: percentage of successive normal-to-normal intervals which exceed 50 ms from previous one; LF: Low frequency; HF: High frequency; LF/HF: Low-frequency/High-frequency ratio; n/r: not reported; ECG: electrocardiogram.
Table 3. Meta-regression models exploring the association between HRV outcomes (HF and LF/HF ratio) and moderators, including age, BMI, and pain type.
Table 3. Meta-regression models exploring the association between HRV outcomes (HF and LF/HF ratio) and moderators, including age, BMI, and pain type.
ModelModeratorHFLF/HF Ratio
β Coefficient (95%CI)p-ValueR2β Coefficient (95%CI)p-ValueR2
Univariate analysisAge0.118 (−0.012, 0.247)0.07563.90.013 (−0.010, 0.035)0.2780.2
BMI−0.088 (−0.173, −0.002)0.04464.40.092 (0.009, 0.0174)0.02982
Type of pain 62.1 0.0
Nociplastic vs. Neuropathic29.08 (−69.96, 128.13)0.565 1.38 (−0.26, 3.01)0.098
Nociceptive vs. Neuropathic29.43 (−69.61, 128.48)0.560 1.479 (0.096, 3.055)0.0657
Abbreviations: HF = High-frequency power; LF/HF = Low-frequency/high-frequency ratio; R2 = proportion of between-study variance explained.
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Daibes, M.; Almarie, B.; Andrade, M.F.; Vidigal, G.d.P.; Aranis, N.; Gianlorenco, A.; Monteiro, C.B.d.M.; Grover, P.; Sparrow, D.; Fregni, F. Do Pain and Autonomic Regulation Share a Common Central Compensatory Pathway? A Meta-Analysis of HRV Metrics in Pain Trials. NeuroSci 2025, 6, 62. https://doi.org/10.3390/neurosci6030062

AMA Style

Daibes M, Almarie B, Andrade MF, Vidigal GdP, Aranis N, Gianlorenco A, Monteiro CBdM, Grover P, Sparrow D, Fregni F. Do Pain and Autonomic Regulation Share a Common Central Compensatory Pathway? A Meta-Analysis of HRV Metrics in Pain Trials. NeuroSci. 2025; 6(3):62. https://doi.org/10.3390/neurosci6030062

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Daibes, Marianna, Bassel Almarie, Maria Fernanda Andrade, Giovanna de Paula Vidigal, Nadine Aranis, Anna Gianlorenco, Carlos Bandeira de Mello Monteiro, Prateek Grover, David Sparrow, and Felipe Fregni. 2025. "Do Pain and Autonomic Regulation Share a Common Central Compensatory Pathway? A Meta-Analysis of HRV Metrics in Pain Trials" NeuroSci 6, no. 3: 62. https://doi.org/10.3390/neurosci6030062

APA Style

Daibes, M., Almarie, B., Andrade, M. F., Vidigal, G. d. P., Aranis, N., Gianlorenco, A., Monteiro, C. B. d. M., Grover, P., Sparrow, D., & Fregni, F. (2025). Do Pain and Autonomic Regulation Share a Common Central Compensatory Pathway? A Meta-Analysis of HRV Metrics in Pain Trials. NeuroSci, 6(3), 62. https://doi.org/10.3390/neurosci6030062

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