Effects of a cardiorespiratory synchronization training mobile application on heart rate variability and electroencephalography in healthy adults

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Effects of a cardiorespiratory synchronization training mobile application on heart rate variability and electroencephalography in healthy adults I-Mei Lin

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Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, Taiwan Department of Medical Research, Kaohsiung Medical University Hospital, Taiwan

ARTICLE INFO

ABSTRACT

Keywords: Cardiorespiratory synchronization training Mobile application Heart rate variability Electroencephalography

Cardiorespiratory synchronization training (CRST) uses diaphragmatic breathing to increase balance in the autonomic nervous system and reduce negative emotions. CRST integrated with high-technology mobile applications affords innovative and convenient home-based training. This study examined the effects of a CRST mobile application on heart rate variability (HRV) and electroencephalography (EEG) parameters in healthy adults. Ninety-six participants were randomly assigned to the CRST, relaxation training (RT; active control group), and control (C) groups. The CRST group received paced breathing training using a wearable device connected to a mobile application and received feedback on the HRV indices. The RT group received muscle relaxation training using a wearable device connected to a mobile application and received feedback on heart rate (HR). The training program was conducted for 1 h per week for 4 weeks. The C group did not receive any wearable device, mobile application, or psychological intervention. Psychological questionnaires on depression and anxiety and physiological measurements of the breathing rates, electrocardiography (ECG), and EEG were measured at the pretest and posttest. The CRST group showed significantly higher HRV indices and lower breathing rates at the posttest than the RT and C groups. There were no significant interaction effects on EEG parameters at pretest and posttest among the three groups. Use of a CRST mobile application increased balance in the autonomic nervous system at the resting state. This clinical evidence-based technologically advanced mobile application could be implemented in future clinical practice.

1. Cardiorespiratory synchronization training and heart rate variability Cardiorespiratory synchronization training (CRST), also called heart rate variability biofeedback (HRV-BF) or respiratory sinus arrhythmia biofeedback (RSA-BF), is a non-invasive psychophysiological intervention with an evidence-based training protocol. CRST combines diaphragmatic breathing and pursed-lip breathing, which involves slow and deep inhalation through the nose and exhalation through the mouth (Vaschillo et al., 2006). Lehrer et al. (2000) developed an HRVBF protocol, which used paced breathing to define participant breathing rates at resonance frequency within five breathing frequencies (6.5, 6, 5.5, 5, and 4.5 breaths/min). The resonance frequency is defined by the highest power spectral density of low frequency (LF) oscillations (or LF peak at 0.1 Hz) while participants are breathing at a particular frequency. Participants were trained to reduce their breathing rates through slow and deep breathing to achieve their target resonance frequency, which results in in-phase oscillation of heart rate (HR) and

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breathing patterns. Previous studies have indicated that when participants breathe at a rate between 4.5 and 6.5 breaths/min, the cardiovascular system inhibits vagal outflow during inhalation, resulting in accelerated HR; the cardiovascular system restores vagal outflow during exhalation, slowing the HR via the release of acetylcholine (Shaffer and Ginsberg, 2017). This HR change, with these points of inhalation and exhalation, outlines respiratory sinus arrhythmia (RSA). RSA oscillation can be exaggerated by slow and deep breathing; then, increased RSA amplitude results in differences between HR maximum and minimum (HRmax-min; Eckberg and Eckberg, 1982; Lehrer et al., 2000). When participants practiced CRST twice daily for 3 months, they showed significant increases in RSA amplitudes, resting baroreflex activity, neuroplasticity in the baroreflex, and balance in the autonomic nervous system (Lehrer and Gevirtz, 2014; Shaffer and Ginsberg, 2017; Vaschillo et al., 2006; Wheat and Larkin, 2010).

Department of Psychology, College of Humanities and Social Sciences, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung City 80708, Taiwan. E-mail address: [email protected].

https://doi.org/10.1016/j.ijpsycho.2018.09.005 Received 7 April 2018; Received in revised form 16 September 2018; Accepted 19 September 2018 0167-8760/ © 2018 Elsevier B.V. All rights reserved.

Please cite this article as: Lin, I.-M., International Journal of Psychophysiology, https://doi.org/10.1016/j.ijpsycho.2018.09.005

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2. Introduction

higher beta value in the whole brain than breathing at 8.4 breaths/min (Stancák et al., 1993). Higher beta values indicated high concentration or cognitive processing or cortical arousal in brain regions (Stancák et al., 1993). These results indicate that slow breathing decreases cortical arousal (beta activity) and increases relaxation in the brain (theta and alpha).

2.1. The theory of slow breathing and HRV Three theories have been proposed to justify how slow breathing increases autonomic function and decreases negative emotion. First, resonance frequency (Lehrer et al., 2000) indicates that slow and deep breathing may stimulate vagal afferent pathways, which affect brain areas related to emotion regulation (e.g., the locus coeruleus, orbitofrontal cortex, insula, hippocampus, and amygdala) and regulate the heart-rate rhythm and strengthen homeostasis in the autonomic nervous system and baroreceptors (Lehrer and Gevirtz, 2014). Therefore, HRV-BF can decrease depression and anxiety through vagal afferent pathways to the frontal cortical area and reduce anxiety and apprehension by reducing frontal cortical activity via a pathway passing through the thalamus (Brown and Gerbarg, 2005; Lehrer et al., 2000). In addition, an increase in vagal outflow regulates homeostasis in the cardiovascular system and increases HRV indices, such as the standard deviation of normal-to-normal beats (SDNN), root mean square of successive differences (rMSSD), total power, and HRmax-min (Lehrer and Gevirtz, 2014), while vagal withdrawal may increase very low frequency (VLF) and decrease LF, and block heart-rate coherence (Lehrer et al., 2000). Second, polyvagal theory (Porges, 2001) indicates that parasympathetic nervous system activity is suppressed under stress while the sympathetic nervous system is activated, which leads to raised heart rate and blood pressure. In contrast, an individual may freeze or show avoidance behavior when experiencing stress, decreased HRV indices, and reduced social engagement, which may consequently influence physical and mental health. Third, the neurovisceral integration model (Thayer and Lane, 2007) considers the autonomic nervous system and the vagal nerve, as the central autonomic network which connects the brainstem (the nucleus of the solitary tract) with other brain structures (e.g., the anterior cingulate cortex, insula, and amygdala), and regulates heart rate. Therefore, under stress, vagal activity is subdued and sympathetic activity is increased leading to a rise in HR and reduction in HRV. Breathing slowly at the resonance frequency may activate the vagal and parasympathetic systems and regulate the cardiovascular system to reduce HR and increase HRV. Additionally, slowing breathing increases the feedback loop between vagal and brain areas to regulate participants' emotions.

2.4. Combining CRST with mobile applications The CRST protocol may be assisted by the use of biofeedback equipment and users can be trained by therapists in hospital or laboratory; however, the time, treatment fee, and location may restrict who can receive CRST. Some of the advantages of integrating a CRST protocol with a mobile application are as follows: (1) > 95% of Americans own some type of cell phone, with 77% being smartphone owners (Pew Research Center, 2017); therefore, combining wearable devices with applications allows for psychological intervention to take place anywhere at any time. (2) Mobile health using wearable devices and applications has grown rapidly in recent years and offers a new platform for health promotion, symptom assessment, psychoeducation, physical signal detection, and treatment progress tracking (Luxton et al., 2011). Little evidence-based research has been performed regarding mobile applications combined with wearable devices and breathing training protocols. Chittaro and Sioni (2014) found that a mobile application with visualization for breathing training had better effects on depth of breathing and participant perception of its effectiveness than traditional voice only. One study used smartphones combined with galvanic skin response biofeedback and found decreases in HR and subjective stress levels (Dillon et al., 2016). There are some limitations in the use of wearable devices combined with applications, including the following. (1) Several applications provide breathing training programs with wearable devices to detect the signals of interbeat intervals (IBIs), HR, or breathing rates; however, the application only shows HR data as feedback and does not transform the IBI or HR data to HRV indices. (2) The application cannot delete HR or IBI artifacts, which may lead to misinterpretation of the results. Therefore, technologically advanced methods for the calculation and transformation of IBIs or HR signals to HRV indices are needed in clinical practice. In addition, to the best of our knowledge, no randomized control trial with an active control group, in which a known and effective treatment is compared to an experimental treatment, has to date examined the effect of a CRST mobile application on HRV indices and EEG parameters. Therefore, the purpose of this study was to examine the treatment effects of CRST on HRV and EEG in healthy adults. The hypotheses of this study were as follows: (1) there would be more beneficial effects on HRV indices and EEG parameters in the CRST group than in the active control group (e.g., muscle relaxation group, RT group) or the control group. (2) Participants in the RT group would have a higher relaxation response in the brain regions than those in the CRST or the control group (C group).

2.2. Slow breathing increases HRV CRST has short- and long-term carry-over effects on HRV indices in healthy adults (Henriques et al., 2011; Paul and Garg, 2012; Siepmann et al., 2008; Wells et al., 2012) and patients with physical illness and mental disorders (Del Pozo et al., 2004; Karavidas et al., 2007; Lin et al., 2015; Siepmann et al., 2008; Zucker et al., 2009). Previous studies have confirmed that HR was significantly decreased in participants whose breathing rates were < 6.5 breaths/min and who underwent between 1 and 11 sessions of 1 h of HRV-BF (Sherlin et al., 2009); the training also increased HRV indices, including SDNN, LF power, rMSSD, HR max-min, and total power (Huang et al., 2018; Lehrer and Gevirtz, 2014; Lin et al., 2015; Lin et al., 2014; Prinsloo et al., 2013; Shaffer and Ginsberg, 2017).

3. Methods 3.1. Participants

2.3. Slow breathing and electroencephalography

Healthy adults were recruited from XXX through e-mail and advertising. The inclusion criteria were that participants were aged 20–65 years, did not have physical or mental disorders, did not use prescription medications, and did not have significant symptoms of depression or anxiety (Beck Depression Inventory-II [BDI-II] and the Beck Anxiety Inventory [BAI] scores were below 14 and 8, respectively). In order to exclude other training effects, participants who practiced yoga, meditation, relaxation, chi gong, mindfulness, or deepbreathing exercises > 3 times per week were excluded from this study. Institutional review board approval was obtained from the ethics

Some studies have examined the effects of paced breathing on electroencephalographic (EEG) parameters. Compared with the resting state, slow breathing (3–6 breaths/min) has been shown to increase alpha power (Bušek and Kemlink, 2005; Dziembowska et al., 2016; Fumoto et al., 2004; Sherlin et al., 2009) and reduce alpha variability (Stancák et al., 1993), increase theta power (Dziembowska et al., 2016; Prinsloo et al., 2013), and decrease beta power (Prinsloo et al., 2013). In contrast, normal breathing (12 and 15 breaths/min) produced a 2

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committee of Kaohsiung Medical University Hospital (KMUH-IRB20140315), and participants who were willing to participate provided informed consent. After completing all research procedures, the participants received approximately $10 at each participation visit. G* power software was used to estimate the sample size before the study (Faul et al., 2007). This was a mixed-model ANOVA design with one between-subjects factor (three groups: CRST, RT, and C groups) and one within-subjects factor (two measurements: pretest and posttest). The power was set as 0.95, the number of groups at 3, the alpha error probability at 0.05, and the partial eta-square at 0.25. The estimation of the total sample was 66. Given an estimated 20% dropout rate, it was concluded that 80 participants would be required for the statistical analysis. Two hundred potential participants were recruited through e-mail and advertising. These participants took part in a telephone interview with a research assistant to check the inclusion criteria. Thirty-seven participants were excluded from this study due to a score higher than 14 or 8 on BDI-II or BAI, respectively, had physical or mental disorders, or their ECG or EEG exhibited physical artifacts at pretest or posttest. Ninety-six healthy adults who met the inclusion criteria were enrolled and randomly assigned into one of three groups with 32 participants in each group: the (1) cardiorespiratory synchronization training group (CRST group), (2) relaxation training group (RT group), and (3) control group (C group). Five participants' data were excluded from statistical analysis. The reasons were that four participants' BDI-II score higher than 14 at posttest in the CRST and C groups, and the posttest raw ECG of one participant in the C group showed arrhythmia (Fig. 1).

et al., 1998). Cronbach's α of the BAI in this study was 0.87. (2) A Chinese version of the BDI-II was used to measure depression; it contains two subscales measuring cognitive and somatic symptoms of depression (Chen, 2000). Cronbach's α for the BDI-II has been reported to range between 0.92 and 0.93 and its 1-week test-retest reliability as 0.93 (Beck et al., 1996; Steer et al., 1998). Cronbach's α of the BDI-II in this study was 0.74. (3) A visual analog scale (VAS) was used to rate subjective tension and relaxation levels with a range from 0 (not at all) to 100 (very; Lin et al., 2014). 3.2.2. Physiological measurements The ECG, breathing rates, and EEG were recorded simultaneously during a 5-min resting state at the pretest and posttest. Participants were instructed to wash their hair the day before the EEG measurement and to not drink coffee or alcoholic beverages for 3 h before the physiological measurements. ECG raw signals were collected using the ProComp Infiniti™ (Thought Technology Ltd., Montreal, QC, Canada) with a sampling rate of 2048 Hz and filtered at 0.001–0.50 Hz. The respiration sensor with a 256 Hz sampling rate was placed on participants' chests to measure the breathing rates. EEG raw signals were collected using a 19-channel EEG cap with linked-ear reference from BrainAvatar (BrainMaster Technologies, Inc., Bedford, Ohio, USA). EEG was recorded based on the International 10–20 system; the sampling rate was 256 Hz, electrode impedances were below 5 kΩ, band-pass filtering was at 1–32 Hz, and notch filtering was at 60 Hz. 3.2.3. Experimental procedure Participants sat in a comfortable chair in a temperature-controlled room (22–24 °C), where they completed the psychological questionnaires. They were then instructed to look at a 14-inch laptop screen and follow the paced breathing. Participants were asked to inhale when a yellow ball went up and exhale when a yellow ball went down. The breathing rates were 6.5, 6.0, 5.5, 5, and 4.5 breaths/min in order to define the participant's resonance frequency based on which breathing rates achieved the highest LF power. After the pretest, participants were randomly assigned to one of the three experimental groups (Table 1). The training programs for the CRST and RT groups were 1 h weekly

3.2. Materials 3.2.1. Psychological questionnaires The demographic characteristics were recorded, namely age, gender, height, weight, and body mass index. The following psychometric tests were administrated at the pretest and posttest: (1) A Chinese version of the BAI, as translated by Lin (2000), was used to measure anxiety symptoms. Previously, Cronbach's α for the BAI was reported as 0.92 and its 1-week test-retest reliability as 0.75 (Beck

Assessed for eligibility (n = 200)

Enrollment Randomized (n = 96)

Excluded (n = 104) Not meeting inclusion criteria (high BDI-II, BAI, physical or mental disorder; n = 37) Declined to participate due to time unavailability or other reasons (n = 67)

Allocation

Follow-up

Allocated to intervention: CRST group (n = 32) Received allocated intervention (n = 31) Did not receive allocated intervention due to high BDI-II (n = 1)

Allocated to intervention: RT group (n = 32) Received allocated intervention (n = 32) Did not receive allocated intervention due to personal reasons (n = 0)

Allocated to intervention: Control group (n = 32) Received allocated intervention (n = 30) Did not receive allocated intervention due to high BDI-II (n = 2)

Lost to follow-up (physical artifacts) (n = 2) Discontinued intervention (n = 0)

Lost to follow-up (physical artifacts) (n = 0) Discontinued intervention (drop-out, n = 3)

Lost to follow-up (under medication) (n = 1) Discontinued intervention (n = 0)

Analyzed (n = 27) Excluded from analysis (n = 2) *High BDI-II (n = 2) *Discontinued intervention (n = 0)

Analyzed (n = 29) Excluded from analysis (n = 0) *High BDI-II (n = 0) *Discontinued intervention (n = 0)

Analyzed (n = 26) Excluded from analysis (n = 3) *Physical articles (n = 1) *High BDI-II (n = 2) *Discontinued intervention (n = 0)

Analysis

Fig. 1. CONSORT flow diagram. 3

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Table 1 Training programs for the CRST and RT groups. CRST group

RT group

C group

Pretest

Pretest

Pretest

Pretest

Week 1

Body screening and muscle relaxation

–

Week 2

Diaphragmatic breathing, participants were guided to step-down their breathing rates with a light bulb animation until they achieved their resonance frequency. This breathing rate was the goal for CRST training Diaphragmatic breathing with pursed-lip breathing, and CRST CRST Posttest

Body scan and muscle relaxation training on legs and bottom Muscle relaxation on back, shoulder, and neck Muscle relaxation on forehead and hands Muscle relaxation for 6 parts of the body Posttest

Week 3 Week 4 Posttest

– – – Posttest

Note: CRST = cardiorespiratory synchronization training; RT = relaxation training; C = control.

for 4 weeks in the laboratory on schedule, if participants could not adhere to the time schedule, research assistants helped them reschedule and complete the training goals within that week. The participants were assigned 10-min homework for 4 weeks and were instructed to upload their training history daily through the application. The therapist analyzed participant HRV indices or HR data and provided feedback at the next training session. The therapist also discussed treatment effectiveness and training problems with the participants. The mobile app for the CRST and RT groups was structured as follows:

measured. D, body scan and muscle relaxation training. E and F, the HR feedback after the RT training. (3) C group: participants did not receive an application or any training program between the pretest and posttest. According to research ethics, they received psychoeducation for muscle relaxation and slow breathing training after the posttest. 3.3. Data analysis

(1) Mobile application for the CRST group: The chest belt of Zephyr™ BioHarness™ (Fig. 2A; Zephyr Technology, Auckland, New Zealand) was connected through Bluetooth to a Zenfone5 mobile phone (ASUS, Taipei, Taiwan). The application screen was designed by the Industrial Technology Research Institute based on the protocol of Lin et al. (2015). The IBIs and breathing rates were collected at a resting state for 5 min before each training session, and the IBI data were transformed to HRV indices and shown on the application's graphical user interface (Fig. 2B–C). Participants were instructed to inhale as the animated light bulb expanded and exhale as the light bulb shrank until achieving their rate of resonance frequency breathing that was measured at the pretest (Fig. 2D–E). The therapist demonstrated to the participants how to practice diaphragmatic and pursed-lip breathing and assisted them in operating the CRST application; the participants could check their training history on the application (Fig. 2F).

3.3.1. Data reduction (1) HRV indices: The CardioPro Infiniti HRV analysis module (Thought Technology) was used to detect the ECG raw signals and acquire the IBI data. This study used the automatic filter to exclude IBIs that differed by > 20% from the previous IBI, the ECG artifacts by visual inspection in a 5-s window. The ECG artifacts were modified by adding, splitting, and averaging pairs of consecutive IBIs. If the ECG artifacts constituted > 5% of the measurement, this participant's data were not included in the data analysis (Cendales-Ayala et al., 2017; Lin et al., 2015). The IBI data were transformed to time and frequency domains of HRV indices. The time domain of HRV indices included SDNN (refers to the total HRV), rMSSD (refers to parasympathetic activation), HR (refers heart beats per min), and HRmax-min. The frequency domain of HRV indices was analyzed by fast Fourier transform, including LF power (0.04–0.15 Hz; refers to baroreflex activity and is influenced by both the sympathetic and parasympathetic nervous systems), HF power (0.15–0.4 Hz; refers to parasympathetic activation), and total power (refers to total HRV) (Thayer et al., 2008; Shaffer and Ginsberg, 2017; Shaffer et al., 2014). The natural logarithm of LF and HF were calculated and group differences in lnLF and lnHF were assessed. (2) EEG parameters: The BrainAvatar analysis (BrainMaster) was used to analyze EEG raw signals by visual inspection in a 10-s window, and delete eye blink and movement artifacts. This study used joint timefrequency analysis to analyze absolute EEG power into the following bands: total alpha (8–12 Hz) and total beta (12–32 Hz) at Fz, Cz, and Pz, which were subsequently transformed to relative EEG (Collura, 2014).

Note: A, the sensor of Zephyr™ BioHarness™ for the CRST group. B, the application guided participants to sit and rest on a chair; the IBIs were measured for 5 min during the resting baseline. C, HRV indices after the baseline measurement. D, instructions for setting up the training duration, breathing rate and pattern for the breathing training. E, breathing training guided by the animated light bulb. F, the training history. (2) Mobile application for the relaxation training (RT) group: A MioAlpha HR watch (Fig. 3A; Johnson Health Tech, Taipei, Taiwan) was connected through Bluetooth to a Zenfone5 mobile phone (ASUS, Taipei, Taiwan). The joiiSports V2.0 application was used (Fig. 3B; JoiiUp Technology Inc., Hsinchu, Taiwan). The HR was measured for 5 min before each training session and the application received the average HR (Fig. 3C). The instructions of body screen and muscle relaxation were played by MP4 from mobile phone. The therapist demonstrated to the participants how to practice body scan and muscle relaxation on six parts of the body (including the legs, bottom, back, shoulder and neck, forehead, and hands) (Fig. 3D) and assisted them in operating the RT protocol and receiving the HR feedback in the application (Fig. 3E–F).

3.4. Statistical analysis One-way analysis of variance (ANOVA) was used to examine the group difference in demographic characteristics, psychological questionnaires, breathing rates, HRV indices, and relative EEG at the pretest among the CRST, RT, and C groups. Mixed-model ANOVA with one between subject factor and one within subject factor was used to examine the three Groups (CRST, RT, and C groups) × two Times (pretest and posttest) interaction effect on the HRV indices (including breathing rates, SDNN, rMSSD, lnLF, lnHF, total power, and HRmax-min) and EEG parameters (including relative total alpha and relative total beta at Fz, Cz, and Pz). Sphericity of variables was assessed prior to undertaking the repeated measures ANOVAs. If violations of sphericity

Note: A, the sensor of the MioAlpha HR watch for the RT group. B, the joilSports app. C, the HR after the 5-min resting baseline was 4

A

A

B

B

D

5

D Fig. 3. Sensor and app protocol for the RT group.

C

Fig. 2. Sensor and application protocol for the CRST group.

C

E

E

F

F

I.-M. Lin

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occurred, the Huynh-Feldt correction was used to correct for Type I error. All p values reported are Huynh-Feldt corrected, and the alpha value was corrected for multiple tests. A p-value < 0.0083 (p = 0.5/6 tests for the mixed-model ANOVAs) was considered statistically significant. Analysis of covariance (ANCOVA) was used to adjust for the value of rMSSD at pretest and subsequently examine the group difference in rMSSD at posttest. The statistical analysis was performed with the Statistical Package for the Social Sciences version 21.0 (International Business Machines Corporation, Armonk, NY, USA). The effect size was determined by partial eta-square (ηp2), with < 0.06 considered a small effect size, 0.06–0.14 a medium effect size, and > 0.14 a large effect size (Cohen, 1988).

4.2. Effects on breathing rates and HRV indices There were significant Group × Time interaction effects on breathing rates, SDNN, lnLF, and HRmax-min (F(2, 79) = 11.53, p < 0.001, ηp2 = 0.23; F(2, 79) = 8.42, p < 0.001, ηp2 = 0.18; and F(2, 2 79) = 10.44, p < 0.001, ηp = 0.21, respectively). Large effect sizes were found for breathing rates, lnLF, and HRmax-min after CRST and RT treatment. There were significant effects over time for the CRST and RT groups for breathing rates, lnLF, and HRmax-min (simple effects analysis: F(1, 79) = 20.77, p < 0.001; F(1, 79) = 33.40, p < 0.001; and F(1, 79) = 22.38, p < 0.001, respectively). Bonferroni-corrected post hoc comparisons revealed that the CRST and RT groups had lower breathing rates, and higher lnLF and HRmax-min at posttest than at pretest. However, the C group did not show a significant time effect (Table 3). As shown in Table 3, Bonferroni-corrected post hoc comparisons found that the CRST group had lower breathing rates at posttest compared to RT and C groups, and the CRST group had higher lnLF and HRmax-min at posttest compared to the C group (Fig. 4). In addition, higher rMSSD at pretest was found in the RT group than in the CRST group. ANCOVA revealed no significant differences among the three groups in rMSSD at posttest after adjusting for rMSSD at pretest (F(2, 78) = 0.022, p = 0.984).

4. Results 4.1. Participant characteristics There was no significant difference between the three groups at the pretest in demographic characteristics (age, gender, education, and dominant hand), psychological questionnaire scores (BAI, BDI-II, VAS_Tension, and TVS_Relaxation), breathing rates, HR, and HRV indices. However, the rMSSD was higher in the RT group than in the CRST group (Table 2).

Table 2 Demographic characteristics, breathing rates, HRV, and EEG between the CRST, RT, and C groups. Variables

CRST group (n = 27)

RT group (n = 29)

C group (n = 26)

F/χ2

p

Age Gender Male Female Education Senior High School Junior College Undergraduate Graduate Dominant hand Left–handed Right–handed Ambidextrous BAI BDI-II VAS–Tension VAS–Relaxation Breathing rates (breaths/min) Heart rate (bpm) HRV indices SDNN (ms) rMSSD LF (ms2) HF (ms2) lnLF (ms2) lnHF (ms2) LF/HF ratio Total power (ms2) HRmax-min (bpm) Relative EEG Fz total alpha Fz total beta Cz total alpha Cz total beta Pz total alpha Pz total beta

25.30 (6.86)

27.24 (10.28)

31.23 (11.18)

3 24

9 20

7 19

F(2,79) = 1.98 χ2(2) = 3.42

0.144 0.181

0 0 19 8

0 2 22 5

1 1 17 7

χ2(6) = 5.17

0.522

1 26 0 2.67 (2.96) 4.70 (3.40) 15.63 (14.74) 77.59 (12.81) 13.34 (3.79) 75.50 (8.18)

1 26 2 1.97 (2.06) 3.79 (3.57) 13.97 (13.98) 78.38 (14.97) 13.88 (2.88) 71.31 (10.41)

0 25 1 2.54 (2.37) 5.04 (3.72) 11.54 (15.67) 76.65 (16.94) 12.69 (3.75) 72.88 (8.77)

χ2(4) = 2.84

0.585

F(2,79) = 0.64 F(2,79) = 0.91 F(2,79) = 0.51 F(2,79) = 0.09 F(2,79) = 0.80 F(2,79) = 1.47

0.532 0.406 0.601 0.913 0.454 0.237

43.48 (15.12) 31.06 (14.37)a 271.82 (372.55) 189.58 (176.39) 5.01 (1.05) 4.90 (0.89) 2.71 (6.44) 686.69 (484.09) 7.81 (3.47)

50.62 (15.87) 43.60 (20.76)a 241.45 (295.30) 382.45 (357.44) 5.01 (0.97) 5.43 (1.18) 1.18 (1.34) 919.59 (543.90) 7.89 (2.760)

45.24 (17.18) 32.35 (16.24) 304.60 (329.29) 252.47 (350.44) 5.05 (1.40) 4.85 (1.26) 2.07 (2.39) 761.69 (525.30) 7.70 (4.00)

F(2,79) = 1.51 F(2,79) = 4.41⁎ F(2,79) = 0.25 F(2,79) = 2.88 F(2,79) = 0.01 F(2,79) = 2.29 F(2,79) = 1.03 F(2,79) = 1.48 F(2,79) = 0.02

0.227 0.015 0.782 0.062 0.989 0.108 0.361 0.235 0.979

13.19 33.19 14.84 33.53 18.72 33.01

12.08 35.11 13.73 35.99 16.21 35.02

14.11 33.76 15.48 33.74 18.52 33.76

F(2,79) = 1.13 F(2,79) = 1.49 F(2,79) = 0.68 F(2,79) = 1.68 F(2,79) = 1.00 F(2,79) = 0.74

0.327 0.233 0.511 0.193 0.372 0.481

(5.68) (3.99) (5.85) (3.34) (8.40) (4.63)

(4.22) (5.83) (5.23) (5.44) (6.63) (6.44)

a

(5.09) (6.20) (5.88) (7.32) (7.19) (7.48)

CRST group < RT group; BAI = Beck Anxiety Inventory; BDI-II = Beck Depression Inventory II; C = control; CRST = cardiorespiratory synchronization training; Cz = central zero; EEG = electroencephalography; Fz = frontal lobe zero; HF = high frequency; HRmax-min = the difference between HR maximum and minimum; HRV = heart rate variability; LF = low frequency; Pz = parietal lobe zero; rMSSD = root mean square of successive differences; RT = relaxation training; SDNN = standard deviation of normal-to-normal beats; VAS = Visual analog scale. ⁎ p < 0.05. 6

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Table 3 Mixed model of ANOVA analysis of breathing rates and HRV indices among the CRST, RT, and C groups across pretest and posttest. Variables

ηp2

Bonferroni post hoc comparisons

11.53⁎⁎⁎ (< 0.001)

0.23

21.23⁎⁎⁎ (< 0.001)

4.64 (0.012)

0.11

1.15 (0.287) 33.40⁎⁎⁎ (< 0.001) 2.00 (0.161) 22.38⁎⁎⁎ (< 0.001)

0.72 (0.487) 8.42⁎⁎⁎ (< 0.001) 2.03 (0.138) 10.44⁎⁎⁎ (< 0.001)

0.02

Group: CRST < RT, C at posttest; Time: posttest > pretest for CRST, RT Group: NS; Time: posttest > pretest for CRST Group: RT > CRST at pretest; Time: NS Group: CRST > C at posttest; Time: post > pre for CRST, RT

CRST group (n = 27)

RT group (n = 29)

C group (n = 26)

F (p)

Pretest

Posttest

Pretest

Posttest

Pretest

Posttest

Group

Time

Group × Time

Breathing rates (cycles/min)

13.34 (3.79)

8.64 (4.70)

13.88 (2.88)

12.39 (4.09)

12.69 (3.75)

13.02 (4.05)

3.24 (0.044)

20.77⁎⁎⁎ (< 0.001)

SDNN (ms)

43.48 (15.12)

61.35 (22.97)

50.62 (15.87)

57.24 (22.96)

45.24 (17.18)

48.65 (15.72)

1.37 (0.260)

rMSSD (ms)

31.06 (14.37) 5.01 (1.05) 4.90 (0.89) 7.81 (3.47)

34.31 (12.07) 6.40 (1.28) 4.43 (0.83) 12.40 (5.57)

43.60 (20.76) 5.01 (0.97) 5.43 (1.18) 7.89 (2.76)

42.65 (20.80) 5.64 (1.22) 5.16 (1.09) 9.56 (5.06)

32.35 (16.24) 5.05 (1.40) 4.85 (1.26) 7.70 (4.00)

35.18 (15.75) 5.18 (1.20) 4.99 (1.13) 7.49 (3.98)

3.87 (0.025) 2.14 (0.125) 2.11 (0.128) 2.93 (0.059)

lnLF (ms2) lnHF (ms2) HRmax-min (bpm)

0.18 0.05 0.21

Group: CRST > C at posttest; Time: post > pre for CRST, RT

Note: C = control; CRST = cardiorespiratory synchronization training; lnHF = natural logarithm of high frequency; HRmax-min = the difference between HR maximum and minimum; HRV = heart rate variability; lnLF = natural logarithm of low frequency; NS = not significant; rMSSD = root mean square of successive differences; RT = relaxation training; SDNN = standard deviation of normal-to-normal beats. ⁎⁎⁎ p < 0.0083 (p value adjusted as 0.05/6 dependent variables = 0.0083).

4.3. Effects on relative EEG at Fz, Cz, and Pz

breathing training with HR and HRV feedback in real time. Participants were able to adjust their breathing rates and patterns and HR, and showed greater beneficial effects in HRV indices, compared with participants in either relaxation training or control groups. Previous studies used SDNN as the HRV index and examined the treatment effectiveness of HRV-BF (Del Pozo et al., 2004; Lin et al., 2015, 2016, 2018). Although the current study did not find significant interaction effects on SDNN, participants in the CRST group increased SDNN by 17.87 ms, whereas participants in the RT and C groups only increased SDNN by 6.62 ms and 3.41 ms, respectively. The increase in the SDNN value (17.87 ms) in the current study was higher than in previous studies. For example, among patients with coronary artery disease, HRV-BF increased SDNN by 8.1 ms (28.0 ms at pretest and 36.1 ms at posttest; Del Pozo et al., 2004) and 6.84 ms (29.16 ms at pretest and 36.00 ms at posttest; Lin et al., 2015). Additionally, an increase of 8.59 ms (24.97 ms at pretest and 33.56 ms at posttest; Lin et al., 2016) was found in patients with heroin use. Further, an increase of 9.34 ms (37.18 at pretest and 46.52 at posttest; Lin et al., 2018) was found in patients with major depressive disorder. Regarding the possible physiological mechanisms underlying the effect of CRST on HRV indices, some studies have confirmed that CRST has short- and long-term carry-over effects on increasing the balance between the sympathetic and parasympathetic nervous systems and improves the homeostasis of baroreflex activity (Laborde et al., 2017; Lehrer and Gevirtz, 2014; McCraty and Shaffer, 2015; Moss and Shaffer, 2017; Quintana and Heathers, 2014; Shaffer et al., 2014; Wheat and Larkin, 2010). This study found that increased LF after CRST may be attributable to participants slowing down their breathing rate from 13.34 breaths/min (SD = 3.79) at the pretest to 8.64 breaths/min (SD = 4.70) at the posttest. When participants breathed at a rate lower than 9 breaths/min, some of the HF power of HRV shifted down into the LF power region (Lehrer and Gevirtz, 2014). The CRST protocol trained participants to breathe slowly during the training session and practice 10 min daily. The breathing exercise may have become an automatic behavioral response during the participants' resting baseline at the posttest. In addition, the CRST protocol trained participants to maximize the magnitude of the RSA oscillation using slow and deep breathing, which caused an increase in the HRmax-min index of HRV (Eckberg and Eckberg, 1982). These relationships between HR and breathing patterns may be very important in physical and mental health. Prior studies suggested that participants learnt how to increase and decrease their HR by breathing training; moreover, the studies indicated that participants' breathing rates decreased, and that there

There was no significant Group × Time interaction effect on relative total alpha at Fz, Cz, or Pz (F(2, 79) = 0.42, p = 0.660, ηp2 = 0.01; F(2, 2 79) = 0.31, p = 0.734, ηp = 0.01; and F(2, 79) = 0.52, p = 0.594, 2 ηp = 0.01, respectively), as well as no significant Group × Time interaction effect on relative total beta at Fz, Cz, or Pz (F(2, 79) = 0.16, p = 0.854, ηp2 < 0.01; F(2, 79) = 0.48, p = 0.621, ηp2 = 0.01; and F(2, 2 79) = 0.92, p = 0.404, ηp = 0.02, respectively). There was a significant effect over time for the CRST, RT, and control groups on relative total alpha at Fz (F(1, 79) = 104.32, p < 0.001). Bonferroni-corrected post hoc comparisons found that the three groups had higher relative total alpha at posttest than at pretest (Table 4). 5. Discussion After undertaking CRST and RT using a mobile application, participants' breathing rates decreased, and lnLF and HRmax-min HRV indices increased. However, there were no significant changes in EEG parameters between pretest and posttest among the three groups. The results of the CRST were consistent with those of previous studies that used HRV-BF with patients with coronary artery disease (Del Pozo et al., 2004; Lin et al., 2015), heroin users (Lin et al., 2016), individuals with major depressive disorder (Karavidas et al., 2007; Lin et al., 2018), healthy college students (Lin et al., 2014), and healthy women (Song and Lehrer, 2003). Large effect sizes were found on lnLF and HRmax-min indices of HRV after CRST and RT treatment. Participants in the CRST group exhibited increased HRV values to a slightly greater degree than participants in the RT group. For example, the mean value of lnLF increased in the CRST group by 1.39 ms2 (from 5.01 ms2 at pretest to 6.40 ms2 at posttest), whereas it increased in the RT group by 0.63 ms2 (5.01 ms2 at pretest to 5.64 ms2 at posttest). Additionally, the mean value of HRmax-min increased by 4.59 bpm in the CRST group (7.81 bpm at pretest to 12.40 bpm at posttest), whereas it increased by 1.67 bpm in the RT group (7.89 bpm at pretest to 9.56 bpm at posttest). Moreover, participants in the CRST group exhibited a greater reduction in the mean breathing rate (4.7 cycles/min; 13.34 cycles/min at pretest to 8.64 cycles/min at posttest), compared with the RT group (reduction of 1.49 cycles/min; 13.88 cycles/min at pretest to 12.39 cycles/min at posttest). Notably, an increase of 0.33 cycles/min in mean breathing rate was found in the control group (12.69 cycles/min at pretest to 13.02 cycles/min at posttest). Therefore, participants who underwent CRST intervention, comprising applied 7

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Fig. 4. Breathing rates, lnLF, and HRmax-min of HRV at pretest and posttest for CRST, RT, and C groups.

was a shift in the HRV power spectrum density from the HF band to the LF band (Del Pozo et al., 2004; Lehrer et al., 2004; Karavidas et al., 2007; Yu et al., 2018). Some studies have indicated beneficial effects of the use of HRV-BF, such as improvement in gas exchange efficiency for asthma (Lehrer et al., 2004), reduction in the number of hospitalization and emergency visits for cardiovascular disease (Yu et al., 2018), and improvement in depressive symptoms (Karavidas et al., 2007). However, whether participants in the CRST group exhibit better long-term

physical or mental health requires further investigation. Although Fz total alpha power increased at posttest in the three groups, there were no significant EEG changes after treatment. This result is inconsistent with previous studies that examined the effect of paced breathing on EEG. A previous study confirmed that normal breathing rates can cause higher EEG beta power, while slow breathing rates can cause lower beta values (Stancák et al., 1993). Breathing rates are negatively correlated with alpha power and the alpha/beta ratio 8

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Table 4 Mixed-model ANOVA of relative total alpha and total beta at midline electrodes between CRST, RT, and C groups across pretest and posttest. Variables (Relative EEG)

Total alpha

Fz Cz Pz

Total beta

Fz Cz Pz

CRST group (n = 27)

RT group (n = 29)

C group (n = 26)

F (p)

Pretest

Posttest

Pretest

Posttest

Pretest

Posttest

Group

Time

Group × Time

6.89 (2.67) 14.84 (5.85) 18.73 (8.40) 33.19 (3.99) 33.53 (3.34) 33.01 (4.63)

12.07 (5.44) 13.56 (5.59) 16.90 (7.94) 33.32 (6.61) 34.18 (6.19) 34.80 (6.82)

6.69 (2.83) 13.73 (5.23) 16.21 (6.63) 35.11 (5.83) 35.99 (5.44) 35.02 (6.44)

12.14 (4.13) 13.71 (5.37) 16.53 (7.20) 34.30 (5.86) 34.97 (4.62) 35.39 (5.51)

8.02 (3.49) 15.48 (5.88) 18.52 (7.19) 32.76 (6.20) 33.74 (7.32) 33.76 (7.48)

14.41 (5.44) 14.95 (5.37) 17.46 (5.78) 32.80 (4.92) 33.92 (5.65) 32.87 (6.36)

2.76 (0.070) 0.72 (0.489) 0.61 (0.547) 1.39 (0.255) 1.27 (0.288) 0.99 (0.375)

104.32⁎⁎⁎ (< 0.001) 0.84 (0.363) 0.94 (0.335) 0.08 (0.776) 0.01 (0.928) 0.29 (0.594)

0.42 (0.660) 0.31 (0.734) 0.52 (0.594) 0.16 (0.854) 0.48 (0.621) 0.92 (0.404)

ηp2

Bonferroni post hoc comparisons

0.01

Time: posttest > pretest for CRST, RT, C

0.01 < 0.01 < 0.01 0.01 0.02

Note: C = control; CRST = cardiorespiratory synchronization training; Cz = central zero; Fz = frontal lobe zero; Pz = parietal lobe zero; RT = relaxation training. ⁎⁎⁎ p < 0.0083 (p value adjusted as 0.05/6 dependent variables = 0.0083).

research funding. I would also like to especially thank Sui-Pi Chen, Ph.D., and Ching-Yu Huang, Ph.D., at the Industrial Technology Research Institute, who assisted with apps and smartphones. I would also like to thank research assistants Ying-Ju Chen, Hsin-Yi Tsai, San-Yu Wang, Hsin-Yi Lin, Ya Ting Hung, Chia-I Ko, and Ting-Chun Chen for their help with data collection; Sheng-Yu Fan, Ph.D., and Chin-Lung Chien, Ph.D. for statistical consultations; and Editage (https://www. editage.com.tw/) for English language editing.

(Prinsloo et al., 2013); this indicates that slow breathing increases alpha power in the EEG. Although breathing rates decreased from 13.34 breaths/min at the pretest to 8.64 breaths/min at the posttest in the CRST group, the breathing rates were still too high to increase alpha power compared to the necessary 3–6 breaths/min (Bušek and Kemlink, 2005; Stancák et al., 1993). This study supports the theory of resonance frequency regarding HRV indices (Lehrer et al., 2000). While participants breathe at their resonance frequency (about 5.5–6 breaths/min), their baroreflex is activated by the vagal afferent pathway, HR oscillations occur in phase with breathing patterns, and increases are observed in RSA amplitude and HRmax-min (Lehrer et al., 2000). However, this study did not support the theory of the central autonomic network (Thayer and Lane, 2007), and did not confirm changes in brain activation, such as increased theta and alpha power and decreased beta EEG parameters, after CRST training. Previous studies using mobile applications have measured HR after exercise or relaxation training, and a limited study explored how slowing breathing rates changes autonomic activation (Chittaro and Sioni, 2014; Dillon et al., 2016). Use of a CRST mobile application is a technologically advanced, innovative, convincing, and novel contribution that has utility as a psychological intervention. Participants can learn how to practice the CRST protocol with a smartphone and wearable devices anywhere, at any time. Some limitations should be acknowledged. First, the CRST mobile application only showed the frequency domain of HRV; future version of the application should be able to represent time domain HRV, such as SDNN and rMSSD. Second, participants in the CRST group decreased their posttest resting baseline breathing rates (from 13.34 to 8.64 breaths/min); however, the RT and C groups (13.88 to 12.39 breaths/min and 12.69 to 13.02 breaths/min, respectively) did not change their breathing rates significantly. Future studies need to control breathing rates in order to examine HRV indices. Third, only four sessions of the CRST protocol may be insufficient to confirm changes in EEG parameters. In conclusion, integrating the CRST protocol with smartphone technology in a psychological intervention improved cardiac autonomic regulation. The advantages of combining the CRST protocol with mobile devices are that it can be practiced anywhere at any time, the cost of smartphones and wearable devices is low, and the protocol conserves time and money. The CRST protocol can be applied in clinical practice to improve physical and mental health in the future.

Funding This study was supported by the Ministry of Science and Technology, Taiwan (grant number: MOST 104-2410-H-037-001). Conflicts of interest I-Mei Lin has received a research grant from the Ministry of Science and Technology; the author declares no conflict of interest. Human and animal rights and informed consent All procedures were in accordance with ethical standards of the responsible committee on human experimentation (intuitional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients included in the study. References Beck, A.T., Steer, R.A., Brown, G.K., 1996. Beck Depression Inventory Manual, 2nd ed. Psychological Corporation, San Antonio, TX. Beck, A.T., Epstein, N., Brown, G., Steer, R.A., 1998. An inventory for measuring clinical anxiety: psychometric properties. J. Consult. Clin. Psychol. 56 (6), 893–897. https:// doi.org/10.1037/0022-006X.56.6.893. Brown, R.P., Gerbarg, P.l., 2005. Sudarshan kriya yogic breathing in the treatment of stress, anxiety, and depression: part II-clinical applications and guidelines. J. Altern. Complement. Med. 11 (4), 711–717. https://doi.org/10.1089/acm.2005.11.711. Bušek, P., Kemlink, D., 2005. The influence of the respiratory cycle on the EEG. Physiol. Res. 54, 327–333. Cendales-Ayala, B., Useche, S.A., Gómez-Ortiz, V., Bocarejo, P., 2017. Bus operators' responses to job strain: an experimental test of the job demand–control model. J. Occup. Health Psychol. 22 (4), 518–527. https://doi.org/10.1037/ocp0000040. Chen, H.Y., 2000. Beck Depression Inventory Version II (Chinese Version). Chinese Behavioral Science Corporation, Taipei. Chittaro, L., Sioni, R., 2014. Evaluating mobile apps for breathing training: the effectiveness of visualization. Comput. Hum. Behav. 40, 56–63. https://doi.org/10.1016/ j.chb.2014.07.049S. Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Erlbaum, Hillsdale, NJ. Collura, T.F., 2014. Technical Foundations of Neurofeedback. Routledge, New York, NY. Del Pozo, J.M., Gevirtz, R.N., Scher, B., Guarneri, E., 2004. Biofeedback treatment increases heart rate variability in patients with known coronary artery disease. Am.

Acknowledgments I thank the Ministry of Science and Technology, Taiwan, for the 9

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