Brief Report: HRV Time Domain Parameters Predict Trauma and Depression Symptom Change in Veterans with PTSD Undergoing Biofeedback 

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Schuman, Pavleta Ognyanova, J. P. Ginsberg, Debra K. Moser This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4456874/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Applied Psychophysiology and Biofeedback → Version 1 posted 9 You are reading this latest preprint version Abstract Heart rate variability (HRV) is an index of cardiac autonomic function and an objective biomarker for stress and health. Improving HRV through biofeedback has proven effective in reducing symptoms of posttraumatic stress disorder (PTSD) and depression in veteran populations. Brief protocols involving fewer sessions can better maximize limited clinic resources; however, there is a dearth of knowledge on the number of clinical sessions needed to significantly reduce trauma and depression symptoms. We conducted a series of linear regression models using baseline, post-intervention, and follow-up data from intervention group participants (N = 18) who engaged in a pilot waitlist-controlled study testing the efficacy of a 3-session mobile app-adapted HRV biofeedback intervention for veterans with PTSD. Based on Nunan et al.’s (2010) short-term norms, we found that pre-intervention RMSSD in the normal range significantly predicted PTSD and depression symptom improvement. Findings suggest the utility of baseline RMSSD as a useful metric for predicting HRV biofeedback treatment outcomes for veterans with PTSD and comorbid depression. Those with below-normal baseline RMSSD may likely need additional sessions to show clinically meaningful symptom improvement. heart rate variability posttraumatic stress veterans biofeedback Introduction Characterized by symptoms of re-experiencing, avoidance, negative cognitions and mood, and hyperarousal, posttraumatic stress disorder (PTSD) is a prevalent mental health condition affecting as high as 29% of U.S. veterans at some point in their lifespan (Department of Veterans Affairs, 2023). Current evidence-based treatments do not fully address physiological responses to stress in veterans with PTSD (Niles et al., 2018), a gap that adjunctive heart rate variability (HRV) biofeedback may fill (Tan et al., 2011; Author et al., 2023). Research shows stress influences HRV (i.e., variation in the cardiac interbeat interval) and HRV can be used to objectively assess stress and health (Kim et al., 2018). Low HRV (i.e., reduced variation) reflects cardiac interbeat monotony, characterized by autonomic nervous system dysfunction and worse health (Shaffer et al., 2014). HRV can be measured using time domain, frequency domain, and nonlinear measures. Both time- and frequency-domain measures are indicators of short-term HRV. Short-term measures (~5 min) are useful for evaluating the effects of brief interventions aimed at improving conditions, such as PTSD, where autonomic function is disrupted leading to overactive sympathetic nervous system activity (Author et al., 2017; Tan et al., 2011). Whereas frequency domain measures the index of absolute or relative power, HRV time-domain measures (e.g., standard deviation of normal-to-normal beats [SDNN] and root mean square of successive differences [RMSSD]) index the interbeat interval variability of the heart (Task Force, 1996). See Author et al. (2017) for a more in-depth discussion of HRV metrics and norms. Research shows that autonomic functioning indexed by HRV is often lower in veterans with PTSD compared to nonveterans (Tan et al., 2011). HRV biofeedback (HRVB) has been identified as an effective intervention for improving HRV, depression, and PTSD symptoms in military populations (Lehrer et al., 2020; Tan et al., 2011; Author et al., 2019). Although brief HRVB interventions can improve posttraumatic stressor and depression symptoms, less is known about the number of clinical sessions needed, and how to discern which veterans would benefit from additional sessions beyond the number offered in predefined protocols (Author et al., 2022a). While acknowledging that HRV is lower in individuals with PTSD, Schenider and Schwerdtfeger (2020) noted the difficulty in isolating a singular physiological marker observable in states of rest and stress that captures the broad pattern of autonomic nervous system dysregulation found in individuals with PTSD. In recent years, accumulating research has begun to illuminate the potential of HRV metrics as a prognostic non-invasive tool that could be used to enhance treatment efficacy for PTSD, as well as identify which patients may need supplemental treatment sessions to achieve optimal symptom reduction. In a study of women with posttraumatic stress symptomatology following pregnancy loss, De Faira Cardoso et al. (2022) found statistically significant associations between scores on a measure of PTSD and SDNN, RMSSD, and PNN50% (i.e., percentage of successive normal to normal beats differing by greater than 50 ms; Author et al., 2017). In a study of PTSD treatment outcomes for adults with comorbid substance use disorders and posttraumatic stress undergoing standard cognitive behavioral therapy (CBT) or CBT combined with cognitive processing therapy, Soder et al. (2019) found higher baseline HF-HRV predicted greater reductions in PTSD symptoms, regardless of treatment type. Study Aim and Hypotheses The study aim for this brief report was to explore the predictive value of baseline HRV time-domain data on PTSD and depression symptom change in veterans with PTSD in response to a brief (3-session) technologically-mediated HRVB intervention that was the subject of a previous study (Author et al., 2022a). In subsequent analyses, Author et al. (2022b) reported results of a correlational analysis between baseline HRV time-domain parameters with PTSD and depression symptom change. SDNN and RMSSD measures at baseline were recoded into dichotomous variables with low (below the normal range) and normal (within the normal range) levels based on short-term HRV norms (Nunan et al., 2010). Guided by the results of that correlational analysis, we hypothesized that: (1) Baseline SDNN and RMSSD levels would significantly predict PTSD symptom change at the end of the treatment compared to baseline. (2) Baseline RMSSD level would significantly predict PTSD symptom change at a 4-week follow-up compared to baseline. (3) Baseline RMSSD level would significantly predict depression symptom change at the end of the treatment compared to baseline. (4) Baseline SDNN and RMSSD levels would significantly predict depression symptom change at a 4-week follow-up compared to baseline. We expected that normal baseline levels would be associated with greater improvement (e.g., a decrease in symptoms). Methods Study Design We analyzed data from a pilot waitlist-controlled 3-session mobile app-adapted HRVB intervention for veterans with PTSD (Author et al., 2022a). Data were collected at baseline, post-intervention, and 4-week follow-up. Participants U.S. Veterans with clinically defined military-related PTSD were recruited from a Veterans Administration hospital and an outpatient Veterans’ Center in a Central Southern US state. Participants not previously diagnosed by a licensed provider were diagnosed with PTSD using the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5; Weathers et al., 2013; 2018), a structured interview designed to evaluate the presence and severity of PTSD symptoms based on criteria established by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Association, 2013). Those with significant psychological or physical illness were excluded. After consenting, participants who met eligibility criteria were randomized to either the intervention or waitlist groups using a random draw method. The study was approved by the institutional review boards at the University of Kentucky and the Lexington Veterans Health Care Administration. Participants in this study were from the treatment group ( N = 18) and were between 29 and 75 ( M = 52.78, SD = 16.24) years old, 16.7 % women ( n = 3), and 11.1% non-White ( n = 2). Measures PTSD symptom severity change was measured using the 20-item Posttraumatic Stress Disorder Checklist-5 (PCL-5; Blevins et al., 2015), a self-report measure that corresponds to the 20 DSM-5 PTSD symptoms. It is rated on a 4-point scale from 0 ( not at all ) to 4 ( extremely ) and summed for a total score, with a recommended cut point of 33 suggestive of a provisional PTSD diagnosis. Depression symptom severity change was measured using the Beck Depression Inventory-II (BDI-II; Beck et al., 1996). The BDI-II includes 21 groups of statements rated on a scale of 0 ( absence of symptoms ) to 3 ( extreme symptoms ). Items are summed for a total score that can range from 0 to 63. Total scores of 0–13 indicate minimal, 14–19 mild, 20–28 moderate, and 29–63 severe depression . Resting HRV measurements were collected using the NeXus-10 physiological monitoring system and analyzed using Biotrace software (Mind Media, B. V., The Netherlands). HRV was measured as interbeat intervals during a resting period of at least 10 minutes and analyzed using time and frequency domain measures (SDNN, RMSSD). Missing data were interpolated. Data Analyses Based on the significance level and magnitude of the correlations between baseline HRV time-domain data and PTSD and depression symptom change (Author et al., 2022b), we chose to test four linear regression models predicting symptom change. Baseline SDNN and RMSSD were dichotomous categorical variables coded 0 for low baseline level (below the normal range) and 1 for normal baseline level (within the normal range). Two were simple linear regressions with dichotomized baseline RMSSD level as a predictor variable and the other two were multiple linear regressions with (dichotomized) baseline SDNN and RMSSD levels as predictors and the backward entry criterion. Results Predicting PTSD Symptom Change We tested two different models predicting PTSD symptom change. The first one is a multiple linear regression predicting PTSD symptom change post-intervention to baseline based on dichotomized baseline SDNN and RMSSD levels. The model was significant, F (2, 15) = 5.88, p = .013, and explained 44% of the variance of PTSD symptom change. However, in the backward entry method, neither of the predictor variables were significant, t = -0.88, p = .394 and t = -2.06, p = .058 for dichotomized baseline SDNN and RMSSD levels respectively, and the model was rejected. The final model using dichotomized baseline RMSSD level as a single predictor was significant, F (1, 16) = 11.15, p = .004, and explained 41% of the variance of PTSD symptom change. RMSSD within the normal range as an ordinal variable was related to a greater decrease in PTSD symptoms (PCL-5 scores). A simple linear regression model testing the prediction of PTSD symptom change at follow-up to baseline using baseline RMSSD level as a predictor was also significant, F (1, 16) = 4.84, p = .043, and explained 23% of the variance of PTSD symptom change. Again, dichotomized baseline RMSSD within the normal range predicted a greater decrease in PTSD symptoms (PCL-5 scores) at follow-up compared to baseline (see Table 1). Predicting Depression Symptom Change We tested a simple linear regression model predicting depression symptom change post-intervention to baseline with dichotomized baseline RMSSD level as a predictor. The model was significant, F (1, 16) = 6.17, p = .024, and explained 28% of the variance of depression symptom change. Baseline RMSSD within the normal range predicted a greater decrease in depression symptoms (BDI scores) post-intervention compared to baseline. Then, we tested a multiple linear regression model predicting depression symptom change at follow-up compared to baseline based on dichotomized baseline SDNN and RMSSD levels. The model was significant, F (2, 15) = 6.36, p = .010, and explained 46% of the variance of depression symptom change. This model was rejected, because only one of the predictor variables was significant, t = -0.61, p = .552 and t = -2.38, p = .031 for baseline SDNN level and baseline RMSSD level respectively. Finally, we tested a single linear regression model with baseline RMSSD level as a predictor variable and it was significant, F (1, 16) = 12.86, p = .002. This model explained 45% of the variance of depression symptom change. Baseline RMSSD within the normal range was related to a greater decrease in depression symptoms (BDI scores) at follow-up (see Table 1). Table 1 Linear Regression Models Predicting PTSD and Depression Symptom Change Predictor variable B SE 95% CI p LL UL PCL-change post-intervention to baseline Constant 0.43 3.14 -6.21 7.08 .892 RMSSD baseline level a -14.80 4.34 -24.20 -5.40 .004 PCL-change follow-up to baseline Constant -1.41 3.61 -9.07 6.25 .702 RMSSD baseline level a -11.24 5.11 -22.07 -0.41 .043 BDI-change post-intervention to baseline Constant -1.66 2.86 -7.73 4.42 .571 RMSSD baseline level a -10.06 4.05 -18.64 -1.47 .024 BDI-change follow-up to baseline Constant -1.62 2.78 -7.52 4.28 .568 RMSSD baseline level a -14.11 3.94 -22.45 -5.77 .002 Note . SDNN = standard deviation of normal-to-normal beats; RMSSD = root mean square of successive differences; PTSD = posttraumatic stress disorder; PCL = PTSD Checklist for DSM-V; BDI = Beck’s Depression Inventory; CI = confidence interval; LL = lower limit; UL = upper limit. a 0 = below the normal range, 1 = within the normal range. Discussion PTSD Symptom Change Predicted by Dichotomized Baseline RMSSD Our first hypothesis that baseline SDNN and RMSSD levels would predict post-intervention PTSD symptom change was partially supported. The only significant predictor was RMSSD which explained more than 40% of the variance of PTSD symptom change post-intervention to baseline. Our second hypothesis that baseline RMSSD level would predict follow-up PTSD symptom change was supported. Baseline RMSSD level explained more than 20% of the variance of PTSD symptom change at follow-up compared to baseline. Depression Symptom Change Predicted by Dichotomized Baseline RMSSD Our third hypothesis that baseline RMSSD level would predict post-intervention depression symptom change was supported. Baseline RMSSD level explained almost 1/3 of the variance of depression symptom change post-intervention to baseline. Our last hypothesis that baseline SDNN and RMSSD levels would predict follow-up depression symptom change was also partially supported. Again, baseline RMSSD level was the only significant predictor that explained more than 40% of the variance of depression symptom change at follow-up compared to baseline. In conclusion, we found baseline RMSSD level to be predictive of PTSD and depression symptom change right after a short 3-session HRVB treatment and at follow-up four weeks after the intervention. Baseline RMSSD within the normal range (Nunan et al., 2010 ) was associated with greater symptom improvement as a result of the HRVB treatment, whereas baseline RMSSD below the normal range was associated with less improvement. Although we did not find baseline SDNN level to be a significant predictor of PTSD and depression symptom change in the presence of baseline RMSSD level, Author et al. (2022b) reported significant and moderate correlations of baseline SDNN level with post-intervention PTSD symptom change ( r = − .53, p < .05) and with follow-up depression symptom change ( r = − .51, p < .05). The reported regression results do not underestimate the relationship between baseline SDNN level and symptom change; however, they suggest that in the presence of both time domain parameters baseline SDNN level does not add any predictive value to the equation. To the best of our knowledge, this is the first study to test the relationship between the baseline level of HRV time-domain parameters and symptom change in a veteran population as a result of HRVB treatment, but there are empirical data on the relationship of RMSSD with depressive and posttraumatic symptomatology (Ge et al., 2020 ; Koch et al., 2019 ; Solorzano et al., 2022 ). For example, Ge et al. ( 2020 ) reported in a recent meta-analysis that PTSD patients had lower RMSSD compared to healthy controls. Also based on a meta-analysis, Koch et al. ( 2019 ) found that unmedicated patients with major depression had lower RMSSD compared to healthy controls. If low RMSSD is characteristic of PTSD and depression, it seems plausible that it would also affect the treatment of the respective symptomatology. In this sense, our results align with previously reported data on the relationship of RMSSD with PTSD and depression and add to the literature on treating PTSD and depression in veterans with HRVB therapy. Limitations and Future Research The results presented in this brief report are based on an unequal distribution between the groups of low and normal baseline SDNN level. We report results of a secondary analysis and, therefore, participants were not intentionally sampled based on their baseline HRV time-domain values. Also, it might be difficult to achieve equal distribution given that veterans with PTSD often have lower HRV (Tan et al., 2011 ). However, additional research with a greater number of participants and potentially equal distribution between the groups with low and normal baseline time-domain parameters is warranted. Conclusion Results suggest the prognostic value of RMSSD as a biomarker useful for refining treatment strategies and improving PTSD outcomes, emphasizing the critical role of psychophysiological feedback to improve the effectiveness of evidence-based PTSD interventions. Clinicians should tailor treatment duration with regard to the baseline measures of the client/patient. Veterans with low baseline HRV (i.e., SDNN and/or RMSSD below the normal range) will likely need additional clinical biofeedback sessions to experience significant improvement in PTSD and depression symptoms. Using baseline HRV to individualize treatment planning can result in more optimal outcomes and best maximize limited resources; therefore, treatment duration for HRVB should be adjusted in relationship to HRV baseline data. Biofeedback protocols should be flexibly adapted to meet clients’ needs. Additional research is needed to assess which veterans would be appropriate for brief HRVB and which veterans require additional clinical sessions to achieve a clinically significant reduction in PTSD and depression symptoms. Declarations Author Contribution D.S., P.O., J.G., and D.M. contributed to drafting the manuscript and reviewing the intellectual content.D.S., J.G., and D.M. contributed to the conception and design of the research.P.O. conducted the analysis and compiled the table. Data Availability The data set for this manuscript has been sent to UKnowledge, the University of Kentucky's institutional data repository and will also be available through a request made to the collaborating author. References American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing. Author et al. (2019). Author et al. (2022a). Author et al. (2022b). Beck, A. T., Steer, R. A., Ball, R., & Ranieri, W. F. (1996). Comparison of Beck Depression Inventories-IA and-II in psychiatric outpatients. Journal of Personality Assessment , 67 (3), 588-597. https://doi.org/10.1207/s15327752jpa6703_13 Blevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K., & Domino, J. L. (2015). The posttraumatic stress disorder checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation.Journal of Traumatic Stress, 28(6), 489-498. https://doi.org/10.1002/jts.22059 De Faria Cardoso, C., Ohe, N. T., Bader, Y., Afify, N., Al-Homedi, Z., Alwedami, S. M., O’Sullivan, S., Campos, L. A., & Baltatu, O. C. (2022). Heart rate variability indices as possible biomarkers for the severity of post-traumatic stress disorder following pregnancy loss. Frontiers in Psychiatry, 12 . https://doi.org/10.3389/fpsyt.2021.700920 Department of Veterans Affairs, National Center for PTSD. (2023). How common is PTSD in veterans? https://www.ptsd.va.gov/understand/common/common_veterans.asp Ge, F., Yuan, M., Li, Y., & Zhang, W. (2020). Posttraumatic stress disorder and alterations in resting heart rate variability: A systematic review and meta-analysis. Psychiatry Investigation, 17 (1), 9-20. https://doi.org/10.30773/pi.2019.0112 Kim, H., Cheon, E. J., Daiseg, B., Lee, Y. H., & Koo, B. (2018). Stress and heart rate variability: A meta-analysis and review of the literature. Psychiatry Investigation, 15 (3), 235-245. https://doi.org/10.30773/pi.2017.08.17 Koch, C., Wilhelm, A., Salzmann, S., Rief, W., & Wilhelm, F. (2019). A meta-analysis of heart rate variability in major depression. Psychological Medicine ,49 (12), 1948 – 1957. https://doi.org/10.1017/S0033291719001351 Lehrer, P., Kaur, K., Sharma, A., Shah, K., Huseby, R., Bhavsar, J., & Zhang, Y. (2020). Heart rate variability biofeedback improves emotional and physical health and performance: A systematic review and meta-analysis. Applied Psychophysiology and Biofeedback, 45 (3), 109–129. https://doi.org/10.1007/s10484-020-09466-z Niles, B. L., Polizzi, C. P., Voelkel, E., Weinstein, E. S., Smidt, K., & Fisher, L. M. (2018). Initiation, dropout, and outcome from evidence-based psychotherapies in a VA PTSD outpatient clinic. Psychological Services, 15 (4), 496-502. https://doi.org/10.1037/ser0000175 Nunan, D., Sandercock, G. R., & Brodie, D. A. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. Pacing and Clinical Electrophysiology, 33 (11), 1407-1417. https://doi.org/10.1111/j.1540-8159.2010.02841.x Schneider, M., & Schwerdtfeger, A. (2020). Autonomic dysfunction in posttraumatic stress disorder indexed by heart rate variability: A meta-analysis. Psychological Medicine, 50 (12), 1937-1948. https://doi.org/10.1017/s003329172000207x Shaffer, F., McCraty, R., & Zerr, C. L. (2014). A healthy heart is not a metronome: An integrative review of the heart's anatomy and heart rate variability . Frontiers in Psychology, 5 , Article 1040. https://doi.org/10.3389/fpsyg.2014.01040 Soder, H. E., Wardle, M. C., Schmitz, J. M., Lane, S. D., Green, C., & Vujanovic, A. A. (2019). Baseline resting heart rate variability predicts post‐traumatic stress disorder treatment outcomes in adults with co‐occurring substance use disorders and post‐traumatic stress. Psychophysiology, 56 (8), Article e13377. https://doi.org/10.1111/psyp.13377 Solorzano, C. S., Violani, C., & Grano, C. (2022). Pre-partum HRV as a predictor of postpartum depression: The potential use of a smartphone application for physiological recordings. Journal of Affective Disorders, 319 , 172-180. https://doi.org/10.1016/j.jad.2022.09.056 Tan, G., Dao, T. K., Farmer, L., Sutherland, R. J., & Gevirtz, R. (2011). Heart rate variability (HRV) and posttraumatic stress disorder (PTSD): A pilot study. Applied Psychophysiology and Biofeedback, 36 (1), 27-35. https://doi.org/10.1007/s10484-010-9141-y Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation, 93 (5), 1043–1065. https://pubmed.ncbi.nlm.nih.gov/8598068/ Weathers, F.W., Blake, D.D., Schnurr, P.P., Kaloupek, D.G., Marx, B.P., & Keane, T.M. (2013). The Clinician-Administered PTSD Scale for DSM-5 (CAPS-5) . [Assessment] Available from www.ptsd.va.gov. Weathers, F. W., Bovin, M. J., Lee, D.J., Sloan, D. M., Schnurr, P. P., Kaloupek, D. G., Keane, T. M., & Marx, B. P. (2018). The Clinician-Administered PTSD Scale for DSM–5 (CAPS-5): Development and initial psychometric evaluation in military veterans. Psychological Assessment , 30 (3), 383-395. https://psycnet.apa.org/doi/10.1037/pas0000486 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Aug, 2024 Read the published version in Applied Psychophysiology and Biofeedback → Version 1 posted Editorial decision: Revision requested 07 Jun, 2024 Reviews received at journal 07 Jun, 2024 Reviews received at journal 31 May, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 28 May, 2024 Reviewers invited by journal 27 May, 2024 Submission checks completed at journal 22 May, 2024 Editor assigned by journal 22 May, 2024 First submitted to journal 21 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Current evidence-based treatments do not fully address physiological responses to stress in veterans with PTSD (Niles et al., 2018), a gap that adjunctive heart rate variability (HRV) biofeedback may fill (Tan et al., 2011; Author et al., 2023).\u003c/p\u003e\n\u003cp\u003eResearch shows stress influences HRV (i.e., variation in the cardiac interbeat interval) and HRV can be used to objectively assess stress and health (Kim et al., 2018). Low HRV (i.e., reduced variation) reflects cardiac interbeat monotony, characterized by autonomic nervous system dysfunction and worse health (Shaffer et al., 2014). HRV can be measured using time domain, frequency domain, and nonlinear measures. Both time- and frequency-domain measures are indicators of short-term HRV. Short-term measures (~5 min) are useful for evaluating the effects of brief interventions aimed at improving conditions, such as PTSD, where autonomic function is disrupted leading to overactive sympathetic nervous system activity (Author et al., 2017; Tan et al., 2011). Whereas frequency domain measures the index of absolute or relative power, HRV time-domain measures (e.g., standard deviation of normal-to-normal beats [SDNN] and root mean square of successive differences [RMSSD]) index the interbeat interval variability of the heart (Task Force, 1996). See Author et al. (2017) for a more in-depth discussion of HRV metrics and norms.\u003c/p\u003e\n\u003cp\u003eResearch shows that autonomic functioning indexed by HRV is often lower in veterans with PTSD compared to nonveterans (Tan et al., 2011). HRV biofeedback (HRVB) has been identified as an effective intervention for improving HRV, depression, and PTSD symptoms in military populations (Lehrer et al., 2020; Tan et al., 2011; Author et al., 2019). Although brief HRVB interventions can improve posttraumatic stressor and depression symptoms, less is known about the number of clinical sessions needed, and how to discern which veterans would benefit from additional sessions beyond the number offered in predefined protocols (Author et al., 2022a).\u003c/p\u003e\n\u003cp\u003eWhile acknowledging that HRV is lower in individuals with PTSD, Schenider and Schwerdtfeger (2020) noted the difficulty in isolating a singular physiological marker observable in states of rest and stress that captures the broad pattern of autonomic nervous system dysregulation found in individuals with PTSD. In recent years, accumulating research has begun to illuminate the potential of HRV metrics as a prognostic non-invasive tool that could be used to enhance treatment efficacy for PTSD, as well as identify which patients may need supplemental treatment sessions to achieve optimal symptom reduction. In a study of women with posttraumatic stress symptomatology following pregnancy loss, De Faira Cardoso et al. (2022) found statistically significant associations between scores on a measure of PTSD and SDNN, \u0026nbsp;RMSSD, and PNN50% (i.e., percentage of successive normal to normal beats differing by greater than 50 ms; Author et al., 2017). In a study of PTSD treatment outcomes for adults with comorbid substance use disorders and posttraumatic stress undergoing standard cognitive behavioral therapy (CBT) or CBT combined with cognitive processing therapy, Soder et al. (2019) found higher baseline HF-HRV predicted greater reductions in PTSD symptoms, regardless of treatment type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Aim and Hypotheses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study aim for this brief report was to explore the predictive value of baseline HRV time-domain data on PTSD and depression symptom change in veterans with PTSD in response to a brief (3-session) technologically-mediated HRVB intervention that was the subject of a previous study (Author et al., 2022a). In subsequent analyses, Author et al. (2022b) reported results of a\u0026nbsp;correlational analysis\u0026nbsp;between baseline HRV time-domain parameters with PTSD and depression symptom change. SDNN and RMSSD measures at baseline were recoded into dichotomous variables with low (below the normal range) and normal (within the normal range) levels based on short-term HRV norms (Nunan et al., 2010). Guided by the results of that\u0026nbsp;correlational analysis,\u0026nbsp;we hypothesized that:\u003c/p\u003e\n\u003cp\u003e(1) Baseline SDNN and RMSSD levels would significantly predict PTSD symptom change at the end of the treatment\u0026nbsp;compared to baseline.\u003c/p\u003e\n\u003cp\u003e(2) \u0026nbsp;Baseline RMSSD level would significantly predict PTSD symptom change at a 4-week follow-up compared to baseline.\u003c/p\u003e\n\u003cp\u003e(3) Baseline RMSSD level would significantly predict depression symptom change at the end of the treatment\u0026nbsp;compared to baseline.\u003c/p\u003e\n\u003cp\u003e(4) Baseline SDNN and RMSSD levels would significantly predict depression symptom change\u0026nbsp;at a 4-week follow-up compared to baseline.\u003c/p\u003e\n\u003cp\u003eWe expected that normal baseline levels would be associated with greater improvement (e.g., a decrease in symptoms).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed data from a pilot waitlist-controlled 3-session mobile app-adapted HRVB intervention for veterans with PTSD (Author et al., 2022a). Data were collected at baseline, post-intervention, and 4-week follow-up.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eU.S. Veterans with clinically defined military-related PTSD were recruited from a Veterans Administration hospital and an outpatient Veterans\u0026rsquo; Center in a Central Southern US state. Participants not previously diagnosed by a licensed provider were diagnosed with PTSD using the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5; Weathers et al., 2013; 2018), a structured interview designed to evaluate the presence and severity of PTSD symptoms based on criteria established by the \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u0026nbsp;\u003c/em\u003e (DSM-5; American Psychiatric Association, 2013). Those with significant psychological or physical illness were excluded. After consenting, participants who met eligibility criteria were randomized to either the intervention or waitlist groups using a random draw method. The study was approved by the institutional review boards at the University of Kentucky and the Lexington Veterans Health Care Administration. Participants in this study were from the treatment group (\u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 18) and were between 29 and 75 (\u003cem\u003eM\u003c/em\u003e = 52.78, \u003cem\u003eSD\u003c/em\u003e = 16.24) years old, 16.7 % women (\u003cem\u003en\u003c/em\u003e = 3), and 11.1% non-White (\u003cem\u003en\u003c/em\u003e = 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePTSD symptom severity change\u0026nbsp;\u003c/em\u003ewas measured using the 20-item\u003cem\u003e\u0026nbsp;\u003c/em\u003ePosttraumatic Stress Disorder\u0026nbsp;Checklist-5 (PCL-5; Blevins et al., 2015), a self-report measure that corresponds to\u0026nbsp;the 20 DSM-5 PTSD symptoms. It is rated on a 4-point scale from 0 (\u003cem\u003enot at all\u003c/em\u003e) to 4 (\u003cem\u003eextremely\u003c/em\u003e) and summed for a total score, with a recommended cut point of 33 suggestive of a provisional PTSD diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDepression symptom severity change\u003c/em\u003e was measured using the Beck Depression Inventory-II (BDI-II; Beck et al., 1996). The BDI-II includes 21 groups of statements rated on a scale of 0 (\u003cem\u003eabsence of symptoms\u003c/em\u003e) to 3 (\u003cem\u003eextreme symptoms\u003c/em\u003e). Items are summed for a total score that can range from 0 to 63. Total scores\u0026nbsp;of\u003cstrong\u003e\u0026nbsp;0\u0026ndash;13 indicate minimal, 14\u0026ndash;19 mild, 20\u0026ndash;28 moderate, and 29\u0026ndash;63 severe depression\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResting HRV\u003c/em\u003e measurements were collected using the NeXus-10 physiological monitoring system and analyzed using Biotrace software (Mind Media, B. V., The Netherlands). HRV was measured as interbeat intervals during a resting period of at least 10 minutes and analyzed using time and frequency domain measures (SDNN, RMSSD). Missing data were interpolated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the significance level and magnitude of the correlations between baseline HRV time-domain data and PTSD and depression symptom change (Author et al., 2022b), we chose to test four linear regression models predicting symptom change. Baseline SDNN and RMSSD were dichotomous categorical variables coded 0 for low baseline level (below the normal range) and 1 for normal baseline level (within the normal range). Two were simple linear regressions with dichotomized baseline RMSSD level as a predictor variable and the other two were multiple linear regressions with (dichotomized) baseline SDNN and RMSSD levels as predictors and the backward entry criterion.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePredicting PTSD Symptom Change\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe tested two different models predicting PTSD symptom change. The first one is a multiple linear regression predicting PTSD symptom change post-intervention to baseline based on dichotomized baseline SDNN and RMSSD levels. The model was significant, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 15)\u003c/sub\u003e = 5.88, \u003cem\u003ep\u003c/em\u003e = .013, and explained 44% of the variance of PTSD symptom change. However, in the backward entry method, neither of the predictor variables were significant, \u003cem\u003et\u003c/em\u003e = -0.88, \u003cem\u003ep\u003c/em\u003e = .394 and \u003cem\u003et\u003c/em\u003e = -2.06, \u003cem\u003ep\u003c/em\u003e = .058 for dichotomized baseline SDNN and RMSSD levels respectively, and the model was rejected. The final model using dichotomized baseline RMSSD level as a single predictor was significant, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 16)\u003c/sub\u003e = 11.15, \u003cem\u003ep\u003c/em\u003e = .004, and explained 41% of the variance of PTSD symptom change. RMSSD within the normal range as an ordinal variable was related to a greater decrease in PTSD symptoms (PCL-5 scores).\u003c/p\u003e\n\u003cp\u003eA \u0026nbsp;simple linear regression model testing the prediction of PTSD symptom change at follow-up to baseline using baseline RMSSD level as a predictor was also significant, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 16)\u003c/sub\u003e = 4.84, \u003cem\u003ep\u003c/em\u003e = .043, and explained 23% of the variance of PTSD symptom change. Again, dichotomized baseline RMSSD within the normal range predicted a greater decrease in PTSD symptoms (PCL-5 scores) at follow-up compared to baseline (see Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicting Depression Symptom Change\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe tested a simple linear regression model predicting depression symptom change post-intervention to baseline with dichotomized baseline RMSSD level as a predictor. The model was significant, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 16)\u003c/sub\u003e = 6.17, \u003cem\u003ep\u003c/em\u003e = .024, and explained 28% of the variance of depression symptom change. Baseline RMSSD within the normal range predicted a greater decrease in depression symptoms (BDI scores) post-intervention compared to baseline.\u003c/p\u003e\n\u003cp\u003eThen, we tested a multiple linear regression model predicting depression symptom change at follow-up compared to baseline based on dichotomized baseline SDNN and RMSSD levels. The model was significant, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(2, 15)\u003c/sub\u003e = 6.36, \u003cem\u003ep\u003c/em\u003e = .010, and explained 46% of the variance of depression symptom change. This model was rejected, because only one of the predictor variables was significant, \u003cem\u003et\u003c/em\u003e = -0.61, \u003cem\u003ep\u003c/em\u003e = .552 and \u003cem\u003et\u003c/em\u003e = -2.38, \u003cem\u003ep\u003c/em\u003e = .031 for baseline SDNN level and baseline RMSSD level respectively. Finally, we tested a single linear regression model with baseline RMSSD level as a predictor variable and it was significant, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e(1, 16)\u003c/sub\u003e = 12.86, \u003cem\u003ep\u003c/em\u003e = .002. This model explained 45% of the variance of depression symptom change. Baseline RMSSD within the normal range was related to a greater decrease in depression symptoms (BDI scores) at follow-up (see Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLinear Regression Models Predicting PTSD and Depression Symptom Change\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.956204379562045%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003ePredictor variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.817518248175183%\" colspan=\"4\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eLL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eUL\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\"\u003e\n \u003cp\u003ePCL-change post-intervention to baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.956204379562045%\" colspan=\"3\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.956204379562045%\" colspan=\"3\"\u003e\n \u003cp\u003eRMSSD baseline level\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e-14.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-24.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-5.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\"\u003e\n \u003cp\u003ePCL-change follow-up to baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.956204379562045%\" colspan=\"3\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e.702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.956204379562045%\" colspan=\"3\"\u003e\n \u003cp\u003eRMSSD baseline level\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e-11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-22.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\"\u003e\n \u003cp\u003eBDI-change post-intervention to baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.956204379562045%\" colspan=\"3\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.956204379562045%\" colspan=\"3\"\u003e\n \u003cp\u003eRMSSD baseline level\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e-10.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-18.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"11\"\u003e\n \u003cp\u003eBDI-change follow-up to baseline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.636197440585008%\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636197440585008%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.636197440585008%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.173674588665447%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.151736745886655%\" colspan=\"2\"\u003e\n \u003cp\u003e-7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.151736745886655%\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.151736745886655%\"\u003e\n \u003cp\u003e\u0026nbsp; .568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.4625228519195612%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.956204379562045%\" colspan=\"3\"\u003e\n \u003cp\u003eRMSSD baseline level\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e-14.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-22.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e-5.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.408759124087592%\" colspan=\"2\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. SDNN = standard deviation of normal-to-normal beats; RMSSD = root mean square of successive differences; PTSD = posttraumatic stress disorder; PCL = PTSD Checklist for DSM-V; BDI = Beck\u0026rsquo;s Depression Inventory; CI = confidence interval; \u003cem\u003eLL\u003c/em\u003e = lower limit; \u003cem\u003eUL\u003c/em\u003e = upper limit.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e 0 = below the normal range, 1 = within the normal range.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePTSD Symptom Change Predicted by Dichotomized Baseline RMSSD\u003c/h2\u003e \u003cp\u003eOur first hypothesis that baseline SDNN and RMSSD levels would predict post-intervention PTSD symptom change was partially supported. The only significant predictor was RMSSD which explained more than 40% of the variance of PTSD symptom change post-intervention to baseline. Our second hypothesis that baseline RMSSD level would predict follow-up PTSD symptom change was supported. Baseline RMSSD level explained more than 20% of the variance of PTSD symptom change at follow-up compared to baseline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDepression Symptom Change Predicted by Dichotomized Baseline RMSSD\u003c/h2\u003e \u003cp\u003eOur third hypothesis that baseline RMSSD level would predict post-intervention depression symptom change was supported. Baseline RMSSD level explained almost 1/3 of the variance of depression symptom change post-intervention to baseline. Our last hypothesis that baseline SDNN and RMSSD levels would predict follow-up depression symptom change was also partially supported. Again, baseline RMSSD level was the only significant predictor that explained more than 40% of the variance of depression symptom change at follow-up compared to baseline.\u003c/p\u003e \u003cp\u003eIn conclusion, we found baseline RMSSD level to be predictive of PTSD and depression symptom change right after a short 3-session HRVB treatment and at follow-up four weeks after the intervention. Baseline RMSSD within the normal range (Nunan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was associated with greater symptom improvement as a result of the HRVB treatment, whereas baseline RMSSD below the normal range was associated with less improvement.\u003c/p\u003e \u003cp\u003eAlthough we did not find baseline SDNN level to be a significant predictor of PTSD and depression symptom change in the presence of baseline RMSSD level, Author et al. (2022b) reported significant and moderate correlations of baseline SDNN level with post-intervention PTSD symptom change (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05) and with follow-up depression symptom change (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05). The reported regression results do not underestimate the relationship between baseline SDNN level and symptom change; however, they suggest that in the presence of both time domain parameters baseline SDNN level does not add any predictive value to the equation.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this is the first study to test the relationship between the baseline level of HRV time-domain parameters and symptom change in a veteran population as a result of HRVB treatment, but there are empirical data on the relationship of RMSSD with depressive and posttraumatic symptomatology (Ge et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Koch et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Solorzano et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For example, Ge et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported in a recent meta-analysis that PTSD patients had lower RMSSD compared to healthy controls. Also based on a meta-analysis, Koch et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that unmedicated patients with major depression had lower RMSSD compared to healthy controls. If low RMSSD is characteristic of PTSD and depression, it seems plausible that it would also affect the treatment of the respective symptomatology. In this sense, our results align with previously reported data on the relationship of RMSSD with PTSD and depression and add to the literature on treating PTSD and depression in veterans with HRVB therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Research\u003c/h2\u003e \u003cp\u003eThe results presented in this brief report are based on an unequal distribution between the groups of low and normal baseline SDNN level. We report results of a secondary analysis and, therefore, participants were not intentionally sampled based on their baseline HRV time-domain values. Also, it might be difficult to achieve equal distribution given that veterans with PTSD often have lower HRV (Tan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, additional research with a greater number of participants and potentially equal distribution between the groups with low and normal baseline time-domain parameters is warranted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResults suggest the prognostic value of RMSSD as a biomarker useful for refining treatment strategies and improving PTSD outcomes, emphasizing the critical role of psychophysiological feedback to improve the effectiveness of evidence-based PTSD interventions. Clinicians should tailor treatment duration with regard to the baseline measures of the client/patient. Veterans with low baseline HRV (i.e., SDNN and/or RMSSD below the normal range) will likely need additional clinical biofeedback sessions to experience significant improvement in PTSD and depression symptoms. Using baseline HRV to individualize treatment planning can result in more optimal outcomes and best maximize limited resources; therefore, treatment duration for HRVB should be adjusted in relationship to HRV baseline data. Biofeedback protocols should be flexibly adapted to meet clients\u0026rsquo; needs. Additional research is needed to assess which veterans would be appropriate for brief HRVB and which veterans require additional clinical sessions to achieve a clinically significant reduction in PTSD and depression symptoms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eD.S., P.O., J.G., and D.M. contributed to drafting the manuscript and reviewing the intellectual content.D.S., J.G., and D.M. contributed to the conception and design of the research.P.O. conducted the analysis and compiled the table.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data set for this manuscript has been sent to UKnowledge, the University of Kentucky's institutional data repository and will also be available through a request made to the collaborating author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Psychiatric Association. (2013). \u003cem\u003eDiagnostic and statistical manual of mental disorders\u003c/em\u003e (5th ed.). American Psychiatric Publishing.\u003c/li\u003e\n\u003cli\u003eAuthor et al. (2019). \u003c/li\u003e\n\u003cli\u003eAuthor et al. (2022a). \u003c/li\u003e\n\u003cli\u003eAuthor et al. (2022b). \u003c/li\u003e\n\u003cli\u003eBeck, A. T., Steer, R. A., Ball, R., \u0026amp; Ranieri, W. F. (1996). Comparison of Beck Depression Inventories-IA and-II in psychiatric outpatients. \u003cem\u003eJournal of Personality Assessment\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(3), 588-597. https://doi.org/10.1207/s15327752jpa6703_13\u003c/li\u003e\n\u003cli\u003eBlevins, C. A., Weathers, F. W., Davis, M. T., Witte, T. K., \u0026amp; Domino, J. L. (2015). The posttraumatic stress disorder checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation.Journal of Traumatic Stress, 28(6), 489-498. https://doi.org/10.1002/jts.22059\u003c/li\u003e\n\u003cli\u003eDe Faria Cardoso, C., Ohe, N. T., Bader, Y., Afify, N., Al-Homedi, Z., Alwedami, S. M., O\u0026rsquo;Sullivan, S., Campos, L. A., \u0026amp; Baltatu, O. C. (2022). Heart rate variability indices as possible biomarkers for the severity of post-traumatic stress disorder following pregnancy loss. \u003cem\u003eFrontiers in Psychiatry, 12\u003c/em\u003e. https://doi.org/10.3389/fpsyt.2021.700920 \u003c/li\u003e\n\u003cli\u003eDepartment of Veterans Affairs, National Center for PTSD. (2023). \u003cem\u003eHow common is PTSD in veterans? \u003c/em\u003ehttps://www.ptsd.va.gov/understand/common/common_veterans.asp\u003c/li\u003e\n\u003cli\u003eGe, F., Yuan, M., Li, Y., \u0026amp; Zhang, W. (2020). Posttraumatic stress disorder and alterations in resting heart rate variability: A systematic review and meta-analysis. \u003cem\u003ePsychiatry Investigation, 17\u003c/em\u003e(1), 9-20. https://doi.org/10.30773/pi.2019.0112\u003c/li\u003e\n\u003cli\u003eKim, H., Cheon, E. J., Daiseg, B., Lee, Y. H., \u0026amp; Koo, B. (2018). Stress and heart rate variability: A meta-analysis and review of the literature. \u003cem\u003ePsychiatry Investigation, 15\u003c/em\u003e(3), 235-245. https://doi.org/10.30773/pi.2017.08.17\u003c/li\u003e\n\u003cli\u003eKoch, C., Wilhelm, A., Salzmann, S., Rief, W., \u0026amp; Wilhelm, F. (2019). A meta-analysis of heart rate variability in major depression. \u003cem\u003ePsychological Medicine ,49\u003c/em\u003e(12), 1948 \u0026ndash; 1957. https://doi.org/10.1017/S0033291719001351\u003c/li\u003e\n\u003cli\u003eLehrer, P., Kaur, K., Sharma, A., Shah, K., Huseby, R., Bhavsar, J., \u0026amp; Zhang, Y. (2020). Heart rate variability biofeedback improves emotional and physical health and performance: A systematic review and meta-analysis. \u003cem\u003eApplied Psychophysiology and Biofeedback, 45\u003c/em\u003e(3), 109\u0026ndash;129. https://doi.org/10.1007/s10484-020-09466-z\u003c/li\u003e\n\u003cli\u003eNiles, B. L., Polizzi, C. P., Voelkel, E., Weinstein, E. S., Smidt, K., \u0026amp; Fisher, L. M. (2018). Initiation, dropout, and outcome from evidence-based psychotherapies in a VA PTSD outpatient clinic. \u003cem\u003ePsychological Services, 15\u003c/em\u003e(4), 496-502. https://doi.org/10.1037/ser0000175\u003c/li\u003e\n\u003cli\u003eNunan, D., Sandercock, G. R., \u0026amp; Brodie, D. A. (2010). A quantitative systematic review of normal values for short-term heart rate variability in healthy adults. \u003cem\u003ePacing and Clinical Electrophysiology, 33\u003c/em\u003e(11), 1407-1417. https://doi.org/10.1111/j.1540-8159.2010.02841.x\u003c/li\u003e\n\u003cli\u003eSchneider, M., \u0026amp; Schwerdtfeger, A. (2020). Autonomic dysfunction in posttraumatic stress disorder indexed by heart rate variability: A meta-analysis. \u003cem\u003ePsychological Medicine, 50\u003c/em\u003e(12), 1937-1948. https://doi.org/10.1017/s003329172000207x\u003c/li\u003e\n\u003cli\u003eShaffer, F., McCraty, R., \u0026amp; Zerr, C. L. (2014). A healthy heart is not a metronome: An integrative review of the heart\u0026apos;s anatomy and heart rate variability\u003cem\u003e. Frontiers in Psychology, 5\u003c/em\u003e, Article 1040. https://doi.org/10.3389/fpsyg.2014.01040\u003c/li\u003e\n\u003cli\u003eSoder, H. E., Wardle, M. C., Schmitz, J. M., Lane, S. D., Green, C., \u0026amp; Vujanovic, A. A. (2019). Baseline resting heart rate variability predicts post‐traumatic stress disorder treatment outcomes in adults with co‐occurring substance use disorders and post‐traumatic stress. \u003cem\u003ePsychophysiology, 56\u003c/em\u003e(8), Article e13377. https://doi.org/10.1111/psyp.13377\u003c/li\u003e\n\u003cli\u003eSolorzano, C. S., Violani, C., \u0026amp; Grano, C. (2022). Pre-partum HRV as a predictor of postpartum depression: The potential use of a smartphone application for physiological recordings. \u003cem\u003eJournal of Affective Disorders, 319\u003c/em\u003e, 172-180. https://doi.org/10.1016/j.jad.2022.09.056\u003c/li\u003e\n\u003cli\u003eTan, G., Dao, T. K., Farmer, L., Sutherland, R. J., \u0026amp; Gevirtz, R. (2011). Heart rate variability (HRV) and posttraumatic stress disorder (PTSD): A pilot study. \u003cem\u003eApplied Psychophysiology and Biofeedback, 36\u003c/em\u003e(1), 27-35. https://doi.org/10.1007/s10484-010-9141-y\u003c/li\u003e\n\u003cli\u003eTask Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996). Heart rate variability: standards of measurement, physiological interpretation and clinical use. \u003cem\u003eCirculation, 93\u003c/em\u003e(5), 1043\u0026ndash;1065. https://pubmed.ncbi.nlm.nih.gov/8598068/\u003c/li\u003e\n\u003cli\u003eWeathers, F.W., Blake, D.D., Schnurr, P.P., Kaloupek, D.G., Marx, B.P., \u0026amp; Keane, T.M. (2013). \u003cem\u003eThe Clinician-Administered PTSD Scale for DSM-5 (CAPS-5)\u003c/em\u003e. [Assessment] Available from www.ptsd.va.gov.\u003c/li\u003e\n\u003cli\u003eWeathers, F. W., Bovin, M. J., Lee, D.J., Sloan, D. M., Schnurr, P. P., Kaloupek, D. G., Keane, T. M., \u0026amp; Marx, B. P. (2018). The Clinician-Administered PTSD Scale for DSM\u0026ndash;5 (CAPS-5): Development and initial psychometric evaluation in military veterans. \u003cem\u003ePsychological Assessment\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(3), 383-395. https://psycnet.apa.org/doi/10.1037/pas0000486 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-psychophysiology-and-biofeedback","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apbi","sideBox":"Learn more about [Applied Psychophysiology and Biofeedback](http://link.springer.com/journal/10484)","snPcode":"10484","submissionUrl":"https://submission.nature.com/new-submission/10484/3","title":"Applied Psychophysiology and Biofeedback","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"heart rate variability, posttraumatic stress, veterans, biofeedback","lastPublishedDoi":"10.21203/rs.3.rs-4456874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4456874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Heart rate variability (HRV) is an index of cardiac autonomic function and an objective biomarker for stress and health. Improving HRV through biofeedback has proven effective in reducing symptoms of posttraumatic stress disorder (PTSD) and depression in veteran populations. Brief protocols involving fewer sessions can better maximize limited clinic resources; however, there is a dearth of knowledge on the number of clinical sessions needed to significantly reduce trauma and depression symptoms. We conducted a series of linear regression models using baseline, post-intervention, and follow-up data from intervention group participants (N = 18) who engaged in a pilot waitlist-controlled study testing the efficacy of a 3-session mobile app-adapted HRV biofeedback intervention for veterans with PTSD. Based on Nunan et al.’s (2010) short-term norms, we found that pre-intervention RMSSD in the normal range significantly predicted PTSD and depression symptom improvement. Findings suggest the utility of baseline RMSSD as a useful metric for predicting HRV biofeedback treatment outcomes for veterans with PTSD and comorbid depression. Those with below-normal baseline RMSSD may likely need additional sessions to show clinically meaningful symptom improvement.","manuscriptTitle":"Brief Report: HRV Time Domain Parameters Predict Trauma and Depression Symptom Change in Veterans with PTSD Undergoing Biofeedback ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 07:23:47","doi":"10.21203/rs.3.rs-4456874/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-07T18:36:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-07T18:06:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-31T14:20:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131093177027687869742304503047755638144","date":"2024-05-29T22:41:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125627061649446865629808382839517718984","date":"2024-05-28T11:38:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-27T21:19:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-22T04:28:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-22T04:28:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Applied Psychophysiology and Biofeedback","date":"2024-05-21T19:54:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"applied-psychophysiology-and-biofeedback","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"apbi","sideBox":"Learn more about [Applied Psychophysiology and Biofeedback](http://link.springer.com/journal/10484)","snPcode":"10484","submissionUrl":"https://submission.nature.com/new-submission/10484/3","title":"Applied Psychophysiology and Biofeedback","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"68ee935d-2ae3-4d4f-ab79-c7288ac01b37","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:33:45+00:00","versionOfRecord":{"articleIdentity":"rs-4456874","link":"https://doi.org/10.1007/s10484-024-09655-0","journal":{"identity":"applied-psychophysiology-and-biofeedback","isVorOnly":false,"title":"Applied Psychophysiology and Biofeedback"},"publishedOn":"2024-08-13 15:57:57","publishedOnDateReadable":"August 13th, 2024"},"versionCreatedAt":"2024-06-07 07:23:47","video":"","vorDoi":"10.1007/s10484-024-09655-0","vorDoiUrl":"https://doi.org/10.1007/s10484-024-09655-0","workflowStages":[]},"version":"v1","identity":"rs-4456874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4456874","identity":"rs-4456874","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}