Applied Psychophysiology and Biofeedback最新文献

Core stabilization training utilizes principles of motor learning to retrain control of the trunk muscles and lead to improvements in chronic non-specific low back pain (CNLBP). To compare the effects of biofeedback sensor and conventional physiotherapist (PT) feedback during core stabilization and activity training in patients with CNLBP. Thirty-eight patients with CNLBP were randomly assigned to Biofeedback (n = 19) or PT feedback (n = 19) groups. Patients continued 12 sessions of combined core stabilization and activity training. An auditory and tactile biofeedback was given using a validated tilt sensor integrated with an application in the Biofeedback group. An experienced PT provided verbal and tactile feedback to maintain the neutral position in the PT Feedback group. The outcomes were; disability (Revised Oswestry Disability Index-RODI), muscle activity (m.transversus abdominis and m.multifidus), pain (Visual Analog Scale-VAS), proprioception error of the trunk, patient beliefs (Fear Avoidance Beliefs Questionnaire-FABQ) and presence of depressive symptoms (Beck Depression Index-BDI), and quality of life (Short Form (SF)-36). The main effect of time were statistically significant on VAS, RODI, m.transversus abdominis and m.multifidus muscle activities, flexion, and extension proprioception error of the trunk, FABQ, BDI, and SF-36 scores in Biofeedback and PT feedback groups (p < 0.05 for all). The time X group interaction was significant on flexion and extension proprioception error of the trunk PT feedback group (consecutively; p = 0.004, p = 0.022). Biofeedback sensor or PT feedback during core stabilization training equally improves pain, disability, muscle activity, depressive symptoms, patient beliefs, and quality of life in patients with CNLBP.

The current study compared the effects of heart rate variability biofeedback (HRV-BF) and electroencephalographic biofeedback (EEG-BF) on sleep, mood, and reaction time. Fourteen highly trained male athletes with sleep disturbances participated in this randomised crossover study. Participants took part in HRV-BF and EEG-BF training, with each condition consisting of eight sessions over 15 days. Polysomnography (PSG) and the Pittsburgh sleep quality index (PSQI) were used to assess sleep quality, the profile of mood states (POMS) questionnaire to monitor mood, and reaction time to measure performance pre and post intervention. HRV-BF training improved PSG sleep efficiency (SE) (P = 0.022, d = 0.35, 95% CI 0.01 to 0.16) and subjective sleep duration (P = 0.011, ES = 0.40) when compared to EEG-BF. Only HRV-BF reduced reaction time pre to post biofeedback training (P = 0.020, d = 0.75, 95% CI 0.006 to 0.059). The PSQI showed that both HRV-BF (P = 0.025, ES = 0.31) and EEG-BF (P = 0.003, ES = 0.32) resulted in improved global PSQI scores. Total mood disturbance was also reduced though HRV-BF (P = 0.001, ES = 0.40) and EEG-BF (P = 0.001, ES = 0.30). HRV-BF and EEG-BF enhanced some subjective parameters of sleep and mood. HRV-BF increased PSG SE and subjective sleep duration more than EEG-BF in highly trained athletes with sleep disturbances.

Short-term heart rate variability (HRV) is increasingly used to assess autonomic nervous system activity and found to be useful for monitoring and providing care due to its quick measurement. With evidence of low HRV associated with chronic diseases, mental disorders, and an increased risk of cardiovascular disease, having normative data of HRV across the age spectrum would be useful for monitoring health and well-being of a population. This study examines HRV of healthy Singapore sample, with ages ranging from 10 to 89 years. Short-term HRV of five minutes was measured from 2,143 participants. 974 males and 1,169 females, and overall HRV was found to be 42.4ms (RMSSD) and 52.0 ms (SDNN) with a further breakdown of HRV by age and gender. Overall HRV declined with age and gender, although gender differences dissipated in the 60s age range onwards, with the 50s age range having the sharpest decline in HRV. Short-term HRV norms were similar to Nunan et al.’s (2010) systematic review in various populations and less similar to Choi et al.’s (2020) study on Koreans.

Resonance frequency breathing is a technique that involves breathing that maximizes heart rate variability. It is specific to individuals and is determined through a procedure taking approximately 30 min, using a procedure that is often best carried out at specialized medical institutions. This is a physical and time-consuming burden because of hospital visits and measurements, particularly for patients with cancer. Therefore it would be beneficial if a procedure can be found to determine resonance frequency from the patient’s physical characteristics, without the need for special assessment procedures. This exploratory cross-sectional study examined the correlation between individual characteristics and resonance frequency in healthy volunteers. Multiple regression analysis was performed with the measured resonance frequency as the target variable and individual characteristic parameters as explanatory variables. The study aims to build an estimation formula for resonance frequency with some of these parameters and assess its validity. In addition, the validity of the formula’s applicability to patients with incurable cancers is assessed. A total of 122 healthy volunteers and 32 patients with incurable cancers were recruited as participants. The median resonance frequency of 154 participants was six breaths per min. Sex and height were selected as explanatory variables associated with the measured resonance frequency in the volunteers. The estimation formula for resonance frequency using individual characteristics was 17.90—0.07 × height for men and 15.88—0.06 × height for women. Adjusted R-squared values were 0.55 for men and 0.47 for women. When the measured resonance frequency in patients with incurable cancers was six breaths per minute or less, the resonance frequency estimated by this formula was slightly larger than the measured ones. Information on individual characteristics, such as sex and height, which can be easily obtained, was useful to construct an estimation formula for resonance frequency.

Brain-Computer Interface (BCI) and Neurofeedback (NF) both rely on the technology to capture brain activity. However, the literature lacks a clear distinction between the two, with some scholars categorizing NF as a special case of BCI while others view BCI as a natural extension of NF, or classify them as fundamentally different entities. This ambiguity hinders the flow of information and expertise among scholars and can cause confusion. To address this issue, we conducted a study comparing BCI and NF from two perspectives: the background and context within which BCI and NF developed, and their system design. We utilized Functional Flow Block Diagram (FFBD) as a system modelling approach to visualize inputs, functions, and outputs to compare BCI and NF at a conceptual level. Our analysis revealed that while NF is a subset of the biofeedback method that requires data from the brain to be extracted and processed, the device performing these tasks is a BCI system by definition. Therefore, we conclude that NF should be considered a specific application of BCI technology. By clarifying the relationship between BCI and NF, we hope to facilitate better communication and collaboration among scholars in these fields.

A study was developed for a limb-different accessible video game controller that utilizes an electromyographic sensor to control gameplay actions. Data was collected from 50 college-aged student participants. This biofeedback-based serious game trains users in a virtual capacity, through the visualization of muscle contraction, via the movement of the video game character. The training platform has been developed to accompany the corresponding electromyographic actuated prosthetic arm device, leveraging the same control scheme to enable the translation of hand gesture states. This study evaluated the controller, user interface, and gameplay to identify training improvement outcomes and user satisfaction. Study participants were divided into two cohorts that differed in their intervention between the pre-test and post-test challenge course. Cohort one had a free play environment that encouraged learning through algorithmically generated track patterns and the use of powerups. In contrast, cohort two repeated the challenge mode, which was made up of a course of rings to jump through and focused on targeted muscle discretization via character jump heights correlated to muscle output. Data were collected to develop and validate training methods and identify overall game satisfaction and usability. The results of this study indicated an increase in the user’s ability to be successful based on time on task with the intervention. The study also evaluated the usability and participant experience with the intervention.

The short-term scaling exponent of detrended fluctuation analysis (DFA-a1) of heart rate variability may be a helpful tool to assess autonomic balance as a prelude to daily, individualized training. For this concept to be useful, between-session reliability should be acceptable. The aim of this study was to explore the reliability of DFA-a1 during a low-intensity exercise session in both a non-fatigued and a fatigued condition in healthy males and females. Ten participants completed two sessions with each containing an exhaustive treadmill ramp protocol. Before and after the fatiguing ramp, a standardized submaximal low-intensity exercise bout was performed during which DFA-a1, heart rate, and oxygen consumption (VO₂) were measured. We compared between-session reliability of all metrics prior to the ramps (i.e., non-fatigued status) and after the first ramp (i.e., fatigued status). Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI), the standard error of measurement, and the smallest worthwhile change (SWC) were determined. The ICC and SWC pre fatiguing ramp were 0.85 (95% CI 0.39–0.96) and 5.5% for DFA-a1, 0.85 (0.38–0.96) and 2.2% for heart rate, and 0.84 (0.31–0.96) and 3.1% for VO₂. Post fatiguing ramp, the ICC and SWC were 0.55 (0.00–0.89) and 7.9% for DFA-a1, 0.91 (0.62–0.98) and 1.6% for heart rate, and 0.80 (0.17–0.95) and 3.0% for VO₂. DFA-a1 shows generally acceptable to good between-session reliability with a SWC of 0.06 and 0.07 (5.5–7.9%) during non-fatigued and fatigued conditions. This suggests that this metric may be useful to inform on training readiness.

The purpose of this study is to investigate the effects of 10-week heart rate variability biofeedback training on basketball skills, free throws, and heart rate variability parameters. Twenty-four basketball players (experimental, n = 12 and control, n = 12) aged 18–24 years volunteered to participate in this study. The experimental group participated in a 10-week heart rate variability biofeedback and basketball training program, while the control group only participated in the 10-week basketball training session. Basketball free-throw performance, basketball skills, and heart rate variability tests were conducted on the experimental and control groups before and after the 10-week intervention. Consequently, we discovered that basketball free-throw performance, breathing frequency, and heart rate variability parameters, which reflect vagal modulation of parasympathetic activity, improved in participants who underwent the 10-week heart rate variability biofeedback and basketball training, and not in those who took basketball training only. Our findings propose that heart rate variability biofeedback, alongside basketball workouts, can contribute to better basketball free-throw performance potentially through improved autonomic nervous system functioning.

Heart rate variability (HRV) is a biomarker of psychological and physiological health with greater variability reflecting greater psychophysiological regulatory capacity. The damaging effects of chronic, heavy alcohol use on HRV have been well explored, with greater alcohol use associated with lower resting HRV. In this study we sought to replicate and extend our previous finding that HRV improves as individuals with alcohol use disorder (AUD) reduce or stop drinking and engage in treatment. With a sample of treatment engaged adults in the first year of a current AUD recovery attempt (N = 42), we used general linear models to explore associations between indices of HRV (dependent variables) and time since last alcoholic drink at study baseline assessed using timeline follow-back (independent variable), with checks for effects of age, medication, and baseline AUD severity. As predicted, HRV increased as a function of time since last drink, however, contrary to hypotheses, HR did not decrease. Effect sizes were largest for HRV indices fully under parasympathetic control, and these significant associations remained after controlling for age, medications, and AUD severity. Because HRV is an indicant of psychophysiological health, as well as self-regulatory capacity that may portend subsequent relapse risk, assessing HRV in individuals entering AUD treatment could provide important information about patient risk. At-risk patients may do well with additional support and may especially benefit from interventions like Heart Rate Variability Biofeedback that exercise the psychophysiological systems regulating brain/cardiovascular communication.