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Brief Report: Baseline HRV Time Domain Parameters Predict Trauma and Depression Symptom Change in Veterans with PTSD Undergoing Biofeedback.
Schuman, Donna L; Ognyanova, Pavleta; Ginsberg, J P; Moser, Debra K.
Afiliação
  • Schuman DL; School of Social Work, University of Texas at Arlington, Box 19129, Arlington, TX, USA. donna.schuman@uta.edu.
  • Ognyanova P; Private Practice, Sofia, Bulgaria.
  • Ginsberg JP; Saybrook University, Oakland, USA.
  • Moser DK; College of Medicine, University of Virginia, Charlottesville, USA.
Article em En | MEDLINE | ID: mdl-39136855
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. (Pacing and Clinical Electrophysiology 331407-1417, 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 or an alternative treatment to show clinically meaningful symptom improvement.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article