Biofeedback Quality in Healthy Volunteers in Paced Breathing Algorithm Starting from the Age Physiological Norm

Кулик, А. Л.; Мартыненко, А. В.; Yabluchansky, N.I.

doi:10.1007/978-3-642-23508-5_106

Cited by 1 publication

(1 citation statement)

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“…A distinct approach to this issue was presented in a study investigating several aspects of the HRV-biofeedback technique and its application in certain disorders and the general population [ 51 , 52 ], where a complex criterion called the Biofeedback Quality Index was proposed. A more straightforward approach was applied in a study comparing the effects of different breathing rates, where participants who did not meet the assigned requirements based on breathing frequency were excluded from the per protocol analysis [ 53 ].…”

Section: Introductionmentioning

confidence: 99%

Novel Methodological Tools for Behavioral Interventions: The Case of HRV-Biofeedback. Sham Control and Quantitative Physiology-Based Assessment of Training Quality and Fidelity

Ratajczak

Hajnowski

Stawicki

et al. 2021

Sensors

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Scientific research on heart rate variability (HRV) biofeedback is burdened by certain methodological issues, such as lack of consistent training quality and fidelity assessment or control conditions that would mimic the intervention. In the present study, a novel sham HRV-biofeedback training was proposed as a credible control condition, indistinguishable from the real training. The Yield Efficiency of Training Index (YETI), a quantitative measure based on the spectral distribution of heart rate during training, was suggested for training quality assessment. A training fidelity criterion derived from a two-step classification process based on the average YETI index and its standard deviation (YETISD) was suggested. We divided 57 young, healthy volunteers into two groups, each subjected to 20 sessions of either real or sham HRV-biofeedback. Five standard HRV measures (standard deviation of the NN (SDNN), root mean square of the standard deviation of the NN (RMSSD), total power, low-frequency (LF), and high-frequency (HF) power) collected at baseline, after 10 and 20 sessions were subjected to analysis of variance. Application of a training fidelity criterion improved sample homogeneity, resulting in a substantial gain in effect sizes of the group and training interactions for all considered HRV indices. Application of methodological amendments, including proper control conditions (such as sham training) and quantitative assessment of training quality and fidelity, substantially improves the analysis of training effects. Although presented on the example of HRV-biofeedback, this approach should similarly benefit other behavioral training procedures that interact with any of the many psychophysiological mechanisms in the human body.

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Section: Introductionmentioning

confidence: 99%