This paper is in the following e-collection/theme issue:

mHealth in a Clinical Setting (624) Mindfulness and Meditation (216) Design and Formative Evaluation of Mobile Apps (706) Cardiac Arrhythmia, Atrial Fibrillation (87) mHealth for Wellness, Behavior Change and Prevention (2922)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44855, first published .
Effectiveness of a Mindfulness Meditation App Based on an Electroencephalography-Based Brain-Computer Interface in Radiofrequency Catheter Ablation for Patients With Atrial Fibrillation: Pilot Randomized Controlled Trial

Effectiveness of a Mindfulness Meditation App Based on an Electroencephalography-Based Brain-Computer Interface in Radiofrequency Catheter Ablation for Patients With Atrial Fibrillation: Pilot Randomized Controlled Trial

Effectiveness of a Mindfulness Meditation App Based on an Electroencephalography-Based Brain-Computer Interface in Radiofrequency Catheter Ablation for Patients With Atrial Fibrillation: Pilot Randomized Controlled Trial

Authors of this article:

Ying He1 Author Orcid Image ;   Zhijie Tang2 Author Orcid Image ;   Guozhen Sun1 Author Orcid Image ;   Cheng Cai1 Author Orcid Image ;   Yao Wang1 Author Orcid Image ;   Gang Yang1 Author Orcid Image ;   ZhiPeng Bao1 Author Orcid Image
Article Authors Cited by (4) Tweetations (4) Metrics

    Original Paper

    1Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    2School of Nursing, Nanjing Medical University, Nanjing, China

    *these authors contributed equally

    Corresponding Author:

    ZhiPeng Bao, MSc

    Department of Cardiology

    The First Affiliated Hospital of Nanjing Medical University

    300 Guangzhou Road, Gulou District

    Nanjing, 210029

    China

    Phone: 86 15895903958

    Fax:86 025 68303041

    Email: baozhipeng1219@163.com


    Background: Radiofrequency catheter ablation (RFCA) for patients with atrial fibrillation (AF) can generate considerable physical and psychological discomfort under conscious sedation. App-based mindfulness meditation combined with an electroencephalography (EEG)-based brain-computer interface (BCI) shows promise as effective and accessible adjuncts in medical practice.

    Objective: This study aimed to investigate the effectiveness of a BCI-based mindfulness meditation app in improving the experience of patients with AF during RFCA.

    Methods: This single-center pilot randomized controlled trial involved 84 eligible patients with AF scheduled for RFCA, who were randomized 1:1 to the intervention and control groups. Both groups received a standardized RFCA procedure and a conscious sedative regimen. Patients in the control group were administered conventional care, while those in the intervention group received BCI-based app–delivered mindfulness meditation from a research nurse. The primary outcomes were the changes in the numeric rating scale, State Anxiety Inventory, and Brief Fatigue Inventory scores. Secondary outcomes were the differences in hemodynamic parameters (heart rate, blood pressure, and peripheral oxygen saturation), adverse events, patient-reported pain, and the doses of sedative drugs used in ablation.

    Results: BCI-based app–delivered mindfulness meditation, compared to conventional care, resulted in a significantly lower mean numeric rating scale (mean 4.6, SD 1.7 [app-based mindfulness meditation] vs mean 5.7, SD 2.1 [conventional care]; P=.008), State Anxiety Inventory (mean 36.7, SD 5.5 vs mean 42.3, SD 7.2; P<.001), and Brief Fatigue Inventory (mean 3.4, SD 2.3 vs mean 4.7, SD 2.2; P=.01) scores. No significant differences were observed in hemodynamic parameters or the amounts of parecoxib and dexmedetomidine used in RFCA between the 2 groups. The intervention group exhibited a significant decrease in fentanyl use compared to the control group, with a mean dose of 3.96 (SD 1.37) mcg/kg versus 4.85 (SD 1.25) mcg/kg in the control group (P=.003).The incidence of adverse events was lower in the intervention group (5/40) than in the control group (10/40), though this difference was not significant (P=.15).

    Conclusions: BCI-based app–delivered mindfulness meditation effectively relieved physical and psychological discomfort and may reduce the doses of sedative medication used in RFCA for patients with AF.

    Trial Registration: ClinicalTrials.gov NCT05306015; https://clinicaltrials.gov/ct2/show/NCT05306015

    JMIR Mhealth Uhealth 2023;11:e44855

    doi:10.2196/44855

    Keywords

    atrial fibrillation; radiofrequency catheter ablation; mindfulness meditation; brain computer interface; mHealth; smartphone app; randomized controlled trial 


    Atrial fibrillation (AF) is recognized as the most common cardiac arrhythmia worldwide, with an estimated prevalence of 2%-4% in adults [1]. Epidemiological studies indicate that AF increases the risk of stroke by 5-fold and the risk of overall mortality by 3.5-fold [2,3]. AF is becoming an increasingly extensive public health problem and causes substantial health economic burden [4-6]. Radiofrequency catheter ablation (RFCA) has become a first-line therapy for AF to improve symptoms, cardiac function, and quality of life, and has been shown to be cost-effective [7-9]. In China, the number of patients with AF is estimated to be approximately 20 million, and the number of RFCAs for AF exceeds 30,000 every year [10,11].

    Considering the longer general anesthesia preparation time, higher economic costs, and potential complications, conscious sedation is used for RFCA at most centers in China [12-14]. However, even under well-tolerated doses of sedative drugs, sedation-related side effects such as nausea, vomiting, and oversedation are common [15]. Furthermore, patients are required to remain motionless and endure radiofrequency energy burning their myocardia for hours during the complex RFCA procedure. Consequently, even under deep sedation, patients may still experience considerable pain, anxiety, fatigue, and other discomforts, which may be associated with poor outcomes [16-18]. Several studies have indicated that nonpharmacological interventions could be ideal adjuncts to sedative drugs, effectively reducing patients' physical or psychological discomfort and the required doses of sedative drugs during medical invasive procedures [19,20].

    Mindfulness meditation originates from Buddhist teachings and refers to a category of techniques used to pay attention to the present moment and accept all that arises without judgment [21]. Numerous studies suggest that mindfulness meditation may hold potential for alleviating pain, fatigue, and negative emotions [22,23]. However, the effects of mindfulness meditation during AF ablation remain uncertain. In recent years, with the rapid development of digital medicine, app-based mindfulness interventions have been preliminarily shown to be effective and accessible [24].

    A brain-computer interface (BCI) is defined as a technology for establishing external information communication and control pathways between the human brain and computers or other electronic devices [25]. Electroencephalography (EEG) is a conventional form of brain signal acquisition, which can be recognized and reflected (usually through visual or auditory signals) by BCI [26]. Studies have shown that EEG-based BCI devices can sense and classify human psychological states, which may facilitate mindfulness meditation practice [27,28].

    This study aimed to determine the effects of a BCI-based mindfulness meditation app on RFCA for patients with AF. The primary hypothesis was that the intervention group would experience significant improvements in perceived pain, anxiety, and fatigue compared to the control group. We also hypothesized that the intervention might decrease the use of sedative drugs and the incidence of adverse events.


    Study Design

    This was a single-center, 2-arm, parallel-group, prospective, pilot randomized controlled trial. Patients were randomized 1:1 to the intervention and control groups using a computer-generated randomization list. Due to the nature of the study design, neither program implementers nor patients could be blinded to the intervention. However, the investigators performing the outcome assessments and data analysis were blinded to the group allocation.

    Participants

    Overview

    Patients were eligible for the study if they were (1) diagnosed with AF, (2) at least 18 years old, (3) undergoing their initial RFCA procedure, and (4) willing to participate in the study. Patients were excluded if they had (1) severe systemic diseases such as malignant tumors, (2) a history of mental illness and cognitive complaints, and (3) difficulty understanding the questionnaire and the study aims. They were also excluded if they experienced drastic changes in their condition during RFCA.

    Sample size calculations were conducted using PASS 2021 (NCSS LLC) software and based on previous studies [29,30]. Power analysis showed that a sample size of 70 participants was sufficient to have 90% statistical power at a 2-sided α of .05 for significance. To account for a 20% loss rate, the planned sample size was 84 participants.

    All patients underwent a standardized RFCA procedure for AF with a 3D mapping system (CARTO 3, Biosense Webster) and were provided with standardized information about the study and the potential benefits and risks of the interventions. Both groups received the same sedative regimen during RFCA, which was adjusted by the interventional physician based on the patient's response to the medication and reported pain levels. The regimen included a single dose of parecoxib (40 mg), fentanyl (1 mcg/kg/hour), and dexmedetomidine as necessary, with dosing adjustments made in accordance with standard pain management procedures at our institution. The fentanyl maintenance infusion rate ranged from 0 to 2 mcg/kg/hour, while the dexmedetomidine maintenance infusion rate ranged from 0 to 1 mcg/kg/hour.

    Intervention Group

    Patients in the intervention group received mindfulness meditation guidance delivered through a Chinese-language interface and voice app (Focus Zen, version 2.1.1) along with a BCI-based headband. A mobile phone and a Samsung tablet device with the preinstalled app were prepared in the cardiac catheterization laboratory. Before ablation, study staff briefly introduced the method and meaning of mindfulness meditation to help patients understand the intervention content. Mindfulness meditation represents a practice of awareness in which the person gradually and purposefully focuses on the present without judgement to achieve a state of deep relaxation [31]. The app's developers designed a 35-minute mindfulness meditation course specifically for patients with AF to help them relax during ablation without affecting the ablation procedure (eg, the course instructed patients to breathe evenly rather than deeply). We provided patients with Bluetooth earphones and set the background sound within the app in accordance with the patients' preferences, such as forest, beach, or rain sounds. During the mindfulness meditation practice, patients were guided by a female voice through the app to relax muscles, regulate breathing, and practice visualization and body scanning. Simultaneously, the app collected EEG information using a headband device that was synchronized with the app through Bluetooth technology (Figure 1). An artificial intelligence algorithm included in the app was used to analyze the EEG data and classify the patient's brain state as active, calm, relaxed, or meditative. The app interface and headband light color were adjusted in accordance with the patients' state. Additionally, the app prompted the patients' current brain state through background sound effects and guided the patient to maintain the state or make adjustments through the app voice.

    ‎
    Figure 1. Screenshots of the study app. (A) Device connection, (B) background sound setting, and (C) mindfulness meditation course).
    Control Group

    The control group received routine care for their ablation procedure and was informed about the procedure of ablation and characteristics of impending pain in ablation, as in the intervention group. Psychological and supportive care were provided in accordance with the patients' needs. However, patients in the control group wore the headband device without using the earphones and did not receive mindfulness meditation guidance provided by the app.

    Outcome Measurements

    Both the intervention and control groups were administered 2 surveys, one 30 minutes before ablation and one within 30 minutes after ablation, to assess pain intensity, fatigue, and anxiety using specific paper questionnaires. Demographic information and characteristics of participants were collected at baseline. The study staff recorded patients' hemodynamic parameters (heart rate, blood pressure, and peripheral oxygen saturation [SpO2]), spontaneously reported pain, the doses of sedative drugs used, and adverse events during ablation.

    The primary outcomes were pain and anxiety levels during ablation and fatigue severity after ablation. The intensity of pain was measured using the numeric rating scale, with scores that ranged from 0 (no pain) to 10 (the worst possible pain) [32,33]. The State Anxiety Inventory (A-State) is a subscale of the State-Trait Anxiety Inventory [34], which is mainly used to assess the anxiety state in a specific situation. The A-State score was used in this study to explore the anxiety level of patients during ablation. We evaluated the patients' fatigue after ablation using the Brief Fatigue Inventory (BFI) [35], a 10-item validated scale. A higher score indicates a greater level of fatigue.

    Secondary outcomes included mean heart rate, blood pressure, and SpO2 during ablation. Adverse events were defined as excessive fluctuations in blood pressure (fluctuations of >50 mm Hg in systolic blood pressure), nausea and vomiting, and vasovagal reaction. The study staff also recorded the number of times of spontaneously reported pain and the doses of sedative drugs used during ablation.

    Statistical Analysis

    Statistical analysis was performed using SPSS (version 22.0; IBM Corp) and based on the intention-to-treat principle with a 2-sided significance level of .05. Data were analyzed using descriptive statistics and checked for the normality of their distribution. Descriptive continuous variables are presented as mean (SD) values and categorical variables as frequency and percentage values. Differences between study groups were analyzed using an independent 2-sample t test for numerical variables and the Mann-Whitney U test, chi-square test, or the Fisher exact test for categorical variables.

    Ethical Considerations

    This study was conducted at the cardiac catheterization laboratory of the First Affiliated Hospital of Nanjing Medical University, Nanjing, China, from April to September 2022. All study patients provided oral or written informed consent. This study was approved by the ethics committee of the First Affiliated Hospital of Nanjing Medical University (2022-SR-086) and registered at ClinicalTrials.gov (NCT05306015). Procedures were conducted in accordance with the tenets of the Declaration of Helsinki.


    Baseline Characteristics

    A total of 84 patients (42 patients each in the intervention and control groups) were enrolled and completed baseline measures in this study. A total of 4 patients (2 each from the intervention and control groups) were excluded from the study. Figure 2 shows the CONSORT (Consolidated Standards of Reporting Trials) diagram for this clinical trial.

    No significant differences were found between the intervention group and the control group in baseline characteristics (Table 1). Neither group had previous experience with practicing mindfulness meditation.

    ‎
    Figure 2. CONSORT (Consolidated Standards of Reporting Trials) flow diagram.
    Table 1. Baseline characteristics of the study participants (N=80).
    CharacteristicsIntervention group (n=40)Control group (n=40)P value
    Age (years), mean (SD)58.1 (10.4)60.0 (11.2).44
    Gender (female), n (%)15 (38)16 (40).82
    Weight (kg), mean (SD)71.3(13.6)69.5(11.2).53
    BMI (kg/m²), mean (SD)24.9 (3.0)24.9 (3.3).97
    LADa (mm), mean (SD)40.6 (6.5)40.8 (4.2).89
    LVEFb (%), mean (SD)62.0 (5.9)60.9 (6.4).40
    Hypertension, n (%)14 (35)16 (40).64
    DMc, n (%)6 (15)4 (10).50
    CHDd, n (%)3 (8)9 (23).06
    Type of AFe, n (%).37

    		Paroxysmal24 (60)20 (50)

    		Persistent16 (40)20 (50)
    NYHAf class, n (%).59

    		Class I32 (80)30 (75)

    		Class II8 (20)10 (25)
    RFCAg time (minutes), mean (SD)40.1 (14.3)42.6 (15.0).44
    RFCA energy (Watts), mean (SD)42.2 (3.8)42.1 (3.6).81
    RFCA temperature (℃), mean (SD)28.8 (3.3)29.5 (4.3).39

    aLAD: left atrium diameter.

    bLVEF: left ventricular ejection fraction.

    cDM: diabetes mellitus.

    dCHD: coronary heart disease.

    eAF: atrial fibrillation.

    fNYHA: New York Heart Association.

    gRFCA: radiofrequency catheter ablation.

    Primary Outcomes

    We found no significant difference in the baseline pain, anxiety, and fatigue scores between the intervention and control groups (Table 2). After the intervention, compared to the control group, there were significant differences in numeric rating scale (mean 4.6, SD 1.7 [intervention group] vs mean 5.7, SD 2.1 [control group]; P=.008) and A-State (mean 36.7, SD 5.5 vs mean 42.3, SD 7.2; P<.001) scores after ablation. The BFI score after ablation was significantly lower in the intervention group than in the control group (mean 3.4, SD 2.3 vs mean 4.7, SD 2.2; P=.01).

    Table 2. NRSa, A-Stateb, and BFIc scores of the intervention and control groups.
    VariableBaselinePost intervention

    		Intervention group, mean (SD)Control group, mean (SD)P valueIntervention group, mean (SD)Control group, mean (SD)P value
    NRS score0.3 (0.5)0.4 (0.5).664.6 (1.7)5.7 (2.1).008
    A-State score30.7 (4.4)31.8 (6.2).3936.7 (5.5)42.3 (7.2)<.001
    BFI score1.4 (1.7)1.2 (1.5).493.4 (2.3)4.7 (2.2).01

    aNRS: numerical rating scale.

    bA-State: State Anxiety Inventory.

    cBFI: Brief Fatigue Inventory.

    Secondary Outcomes

    Between the intervention and control groups in ablation, there were no significant differences in the mean heart rate (mean 87.4, SD 15.7 [intervention group] vs mean 91.1, SD 16.4 [control group] beats per minute; P=.31), systolic blood pressure (mean 127.2, SD 15.7 vs mean 131.9, SD 17.4 mm Hg; P=.21), diastolic blood pressure (mean 81.1, SD 10.5 vs mean 82.7, SD 9.6 mm Hg; P=.49), and SpO2 (mean 98.4%, SD 1.3% vs mean 98.5%, SD 1.2%; P=.71; Figure 3).

    There were no significant differences in parecoxib and dexmedetomidine use between the intervention and control groups during ablation. The intervention group had significantly decreased fentanyl use compared to the control group (P=.003; Table 3). Additionally, patients in the intervention group reported significantly fewer times of pain than those in the control group during ablation (P<.001). The incidence of adverse events in the intervention group was lower than that in the control group, but the difference did not reach statistical significance (P=.15).

    ‎
    Figure 3. Hemodynamic parameters of the 2 groups. bpm: beats per minute; DBP: diastolic blood pressure; HR: heart rate; RFCA: radiofrequency catheter ablation; SBP: systolic blood pressure; SpO2: peripheral oxygen saturation.
    Table 3. Comparison of secondary outcomes between the intervention and control groups.
    Medications and outcomesIntervention group (n=40)Control group (n=40)P value
    Parecoxib (mg), mean (SD)29.0 (18.1)27.0 (19.0).63
    Fentanyl (mcg/kg), mean (SD)3.96 (1.37)4.85 (1.25).003
    Dexmedetomidine (mcg/kg), mean (SD)1.49 (0.82)1.85 (1.00) .57
    Patient reports of pain (times), mean (SD)1.5 (1.4)2.8 (1.7)<.001
    Adverse events, n (%)5 (13)10 (25).15

    Principal Findings

    The purpose of this study was to evaluate the effectiveness of a novel BCI-based mindfulness meditation app for patients with AF to improve their physical and psychological status in RFCA. The selected app provides EEG feedback for mindfulness meditation, which may help patients relax and reduce unpleasant experiences without interfering with the ablation process. The key findings showed significantly lower pain, anxiety, and fatigue scores in the intervention group than among those receiving conventional care. No significant differences were found in the mean heart rate, blood pressure, or SpO2 between groups during RFCA. Additionally, the intervention group had a significant decrease in fentanyl use, while the differences in other sedative drugs were not significant. Although the incidence of adverse events was lower in the intervention group, the difference was not significant.

    Mobile health, via wireless technologies such as smartphone apps and wearable devices, is currently used for patients with AF mostly for screening, management, and rehabilitation [36-39]. Numerous studies have investigated the impact of meditation on cardiac disease and suggest that it may offer potential benefits for cardiovascular health, including reducing blood pressure, improving psychological and physiological responses to stress, and possibly mitigating AF progression by modulating the autonomic nervous system [21,40]. Mobile app–based mindfulness meditation has the potential to serve as an adjunct to the RFCA procedure for patients with AF owing to its low risks, potential benefits, and relatively low cost. Nevertheless, it is essential to acknowledge the costs associated with using a commercialized app and headband device, along with the requirement for labor and its cost to monitor the app. Additionally, caution should be exercised in making claims about the low risk of this intervention, as it is based solely on the findings of this small pilot study. Further research is needed to explore the relationship between meditation and AF ablation. To our knowledge, this is the first study to assess the effectiveness of a mindfulness meditation app together with a BCI-based wearable device for patients with AF during RFCA. This study achieved promising results and indicated that this type of intervention could be easily integrated into the standard RFCA workflow.

    It is well known that RFCA for AF can be accompanied by considerable pain and anxiety when conscious sedation is used, which, however, has potential side effects [15]. Anxiety is common in patients with AF [41]. Patients may experience pain and uncertainty for several hours during RFCA, which could amplify negative emotions such as anxiety and made them feel exhausted. Therefore, it is necessary to monitor and intervene in patients with AF’s anxiety during ablation procedures [16]. Previous studies have indicated that apps based on nonpharmacological interventions could effectively reduce the fatigue, anxiety, pain, and the use of sedative drugs in invasive medical treatment [42-44]. Nørgaard et al [45] examined the effects of visualization together with usual pain medication in comparison with conventional care among patients with AF undergoing RFCA, and found significant reductions in the perception of pain and anxiety, as well as the doses of analgesics used in the intervention group [46]. Wearable devices are drastically changing medical practices nowadays. Roxburgh et al [47] implemented a virtual reality headset for patients with AF undergoing cryoballoon ablation under conscious sedation and found that the virtual reality group had a significantly lower perceived pain score and higher comfort score. In recent years, there has been a growing body of literature examining the effects of mindfulness training supported by EEG feedback. Crivelli et al [48] investigated the potential benefits of EEG-based brain-sensing device–supported mindfulness practices in individuals with mild stress levels and found a significant reduction in stress and anxiety. Similarly, Balconi et al [49] used mindfulness exercises in combination with wearable EEG information sensor devices to explore the effect of reducing overall stress levels in healthy individuals and found significant improvements in physiological (heart rate and variability) and subjective markers of stress (perceived stress, anxiety, and mood states). These findings are in line with those of this study, suggesting that EEG feedback may facilitate meditation by providing real-time information to aid users in achieving a mindfulness state. In our study, we assessed the effectiveness of an intervention based on a mindfulness meditation app combined with a BCI-based wearable device, which provides meditation guidance with synchronous EEG feedback to patients. This intervention has a minimal learning curve without requiring specialist training and provides personalized feedback, which may enhance patient engagement and adherence [50]. The potential mechanisms underlying pain, anxiety, and fatigue relief through mindfulness meditation are likely linked with the ability of meditation to change the activity of the insula, somatosensory cortex, anterior cingulate cortex, and prefrontal cortex. These changes may reduce patients' attention, memory, and perception of physical and psychological discomfort [51,52].

    When patients experience physical and mental discomfort in RFCA, the levels of catecholamines, adrenocorticotropic hormone, prolactin, cortisol, and prostaglandins in their blood may increase [53], which could result in unstable hemodynamic parameters (heart rate, blood pressure, and SpO2) [54] and, thereby, affect the performance of procedures and patient safety. Previous studies have indicated that nonpharmacological interventions could effectively stabilize hemodynamic parameters in invasive operative procedures [42,55]. In this study, however, the between-group differences in mean heart rate, blood pressure, or SpO2 during RFCA did not reach statistical significance. The observed discrepancy between objective and subjective outcomes may be attributed to various factors. One possible explanation is that blood pressure changes induced by meditation through the autonomic nervous system may be a long-term process [21]. Furthermore, the timing of data collection may have influenced the findings, as objective measures were collected during the intervention, whereas subjective data were gathered through self-reports post intervention. It is worth noting that patients with AF frequently experience AF episodes during RFCA procedures, which may lead to variations in heart rate and blood pressure levels and contribute to the lack of significant differences between the groups. Additional research with larger sample sizes may help elucidate the potential impact of the intervention on these objective parameters. Although there was no significant difference, the incidence of adverse events was lower in the intervention group. This indicates a possibility for the potential protective effects of our intervention during RFCA, which merit further investigations.

    Strengths and Limitations

    A strength of this study is that it is the first randomized controlled trial, to our knowledge, to explore the effectiveness of a mindfulness meditation app together with a BCI-based wearable device among patients with AF during RFCA, which adds to the evidence base in the areas of meditation and mobile health. This study was designed rigorously. We provided the same care protocol and implemented the use of wearable devices for both groups, and recorded the time, energy, and temperature of ablation to ensure comparable conditions. In addition, this study was performed in a pragmatic setting and no maximum age for participation was stated, which adds to the generalizability of our findings.

    This study also has some limitations. First, it was not a double-blind trial. Neither study staff nor patients were blinded to the intervention due to the nature of the study design. However, the data analysts were masked to group allocation. Additionally, meditation practice is a long-term process. Even with the help of apps and wearable devices, it takes time to master meditation techniques to reach a meditative state faster. At least one preoperative practice session could be added to the protocol for the intervention group; however, this might affect the comparability of the baseline measures. Lastly, this is a single-center study, thus limiting the generalizability of our findings.

    Conclusions

    In conclusion, this study shows that BCI-based app–delivered mindfulness meditation significantly relieved pain, anxiety, fatigue, and may reduce the doses of sedative medication used during RFCA for AF. Although no significant differences in hemodynamic parameters and the incidence of adverse events were observed, there was a decrease in the incidence of adverse events in the intervention group. Smartphone apps and wearable devices could serve as feasible and promising adjuncts to improve patients with AF’s experience in RFCA.

    Acknowledgments

    This research was supported by the National Natural Science Foundation of China (72074124), Young Scholars Fostering Fund of the First Affiliated Hospital of Nanjing Medical University (PY2022002), and the Clinical Ability Improvement Project of the First Affiliated Hospital of Nanjing Medical University (JSPH-NC-2021-16).

    Authors' Contributions

    YH and ZB contributed to the development and administration of the project. YH, ZB, ZT, CC, and GY assisted with implementing the protocol, collecting data, performing statistical analyses, and writing the manuscript. ZB and GS contributed to the conceptualization, methodology, supervision, and funding acquisition. All authors provided edits to the manuscript.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    CONSORT-eHEALTH checklist (V 1.6.1).

    PDF File (Adobe PDF File), 1295 KB
    1. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C, et al. 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). Revista Española de Cardiología (English Edition) 2021 May;74(5):437. [CrossRef]
    2. January CT, Wann LS, Calkins H, Chen LY, Cigarroa JE, Cleveland JC, et al. 2019 AHA/ACC/HRS focused update of the 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines and the Heart Rhythm Society. J Am Coll Cardiol 2019 Jul 09;74(1):104-132 [FREE Full text] [CrossRef] [Medline]
    3. Magnussen C, Niiranen TJ, Ojeda FM, Gianfagna F, Blankenberg S, Njølstad I, BiomarCaRE Consortium. Sex differences and aimilarities in atrial fibrillation epidemiology, risk factors, and mortality in community cohorts: results from the BiomarCaRE Consortium (Biomarker for Cardiovascular Risk Assessment in Europe). Circulation 2017 Oct 24;136(17):1588-1597 [FREE Full text] [CrossRef] [Medline]
    4. Wang L, Ze F, Li J, Mi L, Han B, Niu H, et al. Trends of global burden of atrial fibrillation/flutter from Global Burden of Disease Study 2017. Heart 2021 Jun 04;107(11):881-887. [CrossRef] [Medline]
    5. Sheikh A, Patel NJ, Nalluri N, Agnihotri K, Spagnola J, Patel A, et al. Trends in hospitalization for atrial fibrillation: epidemiology, cost, and implications for the future. Prog Cardiovasc Dis 2015 Sep;58(2):105-116. [CrossRef] [Medline]
    6. Rahman F, Kwan GF, Benjamin EJ. Global epidemiology of atrial fibrillation. Nat Rev Cardiol 2014 Nov 12;11(11):639-654. [CrossRef] [Medline]
    7. Chew DS, Li Y, Cowper PA, Anstrom KJ, Piccini JP, Poole JE, et al. Cost-effectiveness of catheter ablation versus antiarrhythmic drug therapy in atrial fibrillation: the CABANA randomized clinical trial. Circulation 2022 Aug 16;146(7):535-547. [CrossRef]
    8. Liu Y, Liu Q, Yang Y, Zhang C, Yin H, Wu J, et al. Effect of radiofrequency catheter ablation on left atrial structure and function in patients with different types of atrial fibrillation. Sci Rep 2022 Jun 09;12(1):9511 [FREE Full text] [CrossRef] [Medline]
    9. Morillo CA, Verma A, Connolly SJ, Kuck KH, Nair GM, Champagne J, RAAFT-2 Investigators. Radiofrequency ablation vs antiarrhythmic drugs as first-line treatment of paroxysmal atrial fibrillation (RAAFT-2): a randomized trial. JAMA 2014 Feb 19;311(7):692-700. [CrossRef] [Medline]
    10. Shi S, Tang Y, Zhao Q, Yan H, Yu B, Zheng Q, China Atrial Fibrillation Center Project Team). Prevalence and risk of atrial fibrillation in China: a national cross-sectional epidemiological study. Lancet Reg Health West Pac 2022 Jun;23:100439 [FREE Full text] [CrossRef] [Medline]
    11. Writing committee of the report on cardiovascular health and diseases in China. Report on cardiovascular health and diseases in China 2021: an updated summary. Biomed Environ Sci 2022 Jul 20;35(7):573-603 [FREE Full text] [CrossRef] [Medline]
    12. Wang Z, Jia L, Shi T, Liu C. General anesthesia is not superior to sedation in clinical outcome and cost-effectiveness for ablation of persistent atrial fibrillation. Clin Cardiol 2021 Feb 29;44(2):218-221 [FREE Full text] [CrossRef] [Medline]
    13. Hummel J, Elsayed-Awad H. Walking the tightrope between deep sedation and general anesthesia: by whom can this safely be done? J Cardiovasc Electrophysiol 2011 Dec;22(12):1344-1345. [CrossRef] [Medline]
    14. Di Biase L, Saenz LC, Burkhardt DJ, Vacca M, Elayi CS, Barrett CD, et al. Esophageal capsule endoscopy after radiofrequency catheter ablation for atrial fibrillation. Circ: Arrhythmia and Electrophysiology 2009 Apr;2(2):108-112. [CrossRef]
    15. Smith HS, Laufer A. Opioid induced nausea and vomiting. Eur J Pharmacol 2014 Jan 05;722:67-78. [CrossRef] [Medline]
    16. Koleck TA, Mitha SA, Biviano A, Caceres BA, Corwin EJ, Goldenthal I, et al. Exploring depressive symptoms and anxiety among patients with atrial fibrillation and/or flutter at the time of cardioversion or ablation. J Cardiovasc Nurs 2021;36(5):470-481. [CrossRef]
    17. Wasserlauf J, Kaplan RM, Walega DR, Arora R, Chicos AB, Kim SS, et al. Patient-reported outcomes after cryoballoon ablation are equivalent between moderate sedation and general anesthesia. J Cardiovasc Electrophysiol 2020 Jul 27;31(7):1579-1584 [FREE Full text] [CrossRef] [Medline]
    18. Attanasio P, Huemer M, Shokor Parwani A, Boldt LH, Mügge A, Haverkamp W, et al. Pain reactions during pulmonary vein isolation under deep sedation: cryothermal versus radiofrequency ablation. Pacing Clin Electrophysiol 2016 May 31;39(5):452-457. [CrossRef] [Medline]
    19. Nørgaard MW, Pedersen PU, Bjerrum M. Visualisation during ablation of atrial fibrillation - stimulating the patient's own resources: patients' experiences in relation to pain and anxiety during an intervention of visualisation. Eur J Cardiovasc Nurs 2015 Dec 04;14(6):552-559. [CrossRef] [Medline]
    20. Lang EV, Benotsch EG, Fick LJ, Lutgendorf S, Berbaum ML, Berbaum KS, et al. Adjunctive non-pharmacological analgesia for invasive medical procedures: a randomised trial. Lancet 2000 Apr;355(9214):1486-1490. [CrossRef]
    21. Levine GN, Lange RA, Bairey‐Merz CN, Davidson RJ, Jamerson K, Mehta PK, et al. Meditation and cardiovascular risk reduction. JAHA 2017 Oct 11;6(10):e002218. [CrossRef]
    22. Izgu N, Gok Metin Z, Karadas C, Ozdemir L, Metinarikan N, Corapcıoglu D. Progressive muscle relaxation and mindfulness meditation on neuropathic pain, fatigue, and quality of life in patients with type 2 diabetes: a randomized clinical trial. J Nurs Scholarsh 2020 Sep;52(5):476-487. [CrossRef] [Medline]
    23. Thomas Z, Novak M, Platas SGT, Gautier M, Holgin AP, Fox R, et al. Brief mindfulness meditation for depression and anxiety symptoms in patients undergoing hemodialysis. CJASN 2017 Oct 12;12(12):2008-2015. [CrossRef] [Medline]
    24. Mani M, Kavanagh DJ, Hides L, Stoyanov SR. Review and evaluation of mindfulness-based iPhone apps. JMIR Mhealth Uhealth 2015 Aug 19;3(3):e82 [FREE Full text] [CrossRef] [Medline]
    25. Mridha MF, Das SC, Kabir MM, Lima AA, Islam MR, Watanobe Y. Brain-computer interface: advancement and challenges. Sensors (Basel) 2021 Aug 26;21(17):5746 [FREE Full text] [CrossRef] [Medline]
    26. Daly JJ, Huggins JE. Brain-computer interface: current and emerging rehabilitation applications. Arch Phys Med Rehabil 2015 Mar;96(3 Suppl):S1-S7 [FREE Full text] [CrossRef] [Medline]
    27. Torres EP, Torres EA, Hernández-Álvarez M, Yoo SG. EEG-based BCI emotion recognition: a survey. Sensors (Basel) 2020 Sep 07;20(18):5083 [FREE Full text] [CrossRef] [Medline]
    28. Devipriya A, Nagarajan N. A novel method of segmentation and classification for meditation in health care systems. J Med Syst 2018 Sep 11;42(10):193. [CrossRef] [Medline]
    29. Pronk Y, Peters MCWM, Sheombar A, Brinkman J. Effectiveness of a mobile eHealth app in guiding patients in pain control and opiate use after total knee replacement: randomized controlled trial. JMIR Mhealth Uhealth 2020 Mar 13;8(3):e16415 [FREE Full text] [CrossRef] [Medline]
    30. Jibb LA, Stevens BJ, Nathan PC, Seto E, Cafazzo JA, Johnston DL, et al. Implementation and preliminary effectiveness of a real-time pain management smartphone app for adolescents with cancer: A multicenter pilot clinical study. Pediatr Blood Cancer 2017 Oct;64(10):1-9. [CrossRef] [Medline]
    31. Smith JL, Allen JW, Haack C, Wehrmeyer K, Alden K, Lund MB, et al. The impact of app-delivered mindfulness meditation on functional connectivity and self-reported mindfulness among health profession trainees. Mindfulness (N Y) 2021 Oct 07;12(1):92-106 [FREE Full text] [CrossRef] [Medline]
    32. Chiu LYL, Sun T, Ree R, Dunsmuir D, Dotto A, Ansermino JM, et al. The evaluation of smartphone versions of the visual analogue scale and numeric rating scale as postoperative pain assessment tools: a prospective randomized trial. Can J Anaesth 2019 Jun 22;66(6):706-715. [CrossRef] [Medline]
    33. Blödt S, Pach D, Eisenhart-Rothe SV, Lotz F, Roll S, Icke K, et al. Effectiveness of app-based self-acupressure for women with menstrual pain compared to usual care: a randomized pragmatic trial. Am J Obstet Gynecol 2018 Feb;218(2):227.e1-227.e9 [FREE Full text] [CrossRef] [Medline]
    34. Liu W, Mao Y, Wei D, Yang J, Du X, Xie P, et al. Structural asymmetry of dorsolateral prefrontal cortex correlates with depressive symptoms: evidence from healthy individuals and patients with major depressive disorder. Neurosci Bull 2016 Jun 25;32(3):217-226 [FREE Full text] [CrossRef] [Medline]
    35. Park S, Sato Y, Takita Y, Tamura N, Ninomiya A, Kosugi T, et al. Mindfulness-based cognitive therapy for psychological distress, fear of cancer recurrence, fatigue, spiritual well-being, and quality of life in patients with breast cancer-a randomized controlled trial. J Pain Symptom Manage 2020 Aug;60(2):381-389. [CrossRef] [Medline]
    36. Rizas KD, Freyer L, Sappler N, von Stülpnagel L, Spielbichler P, Krasniqi A, et al. Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial. Nat Med 2022 Sep 28;28(9):1823-1830. [CrossRef] [Medline]
    37. Cai C, Bao Z, Wu N, Wu F, Sun G, Yang G, et al. A novel model of home-based, patient-tailored and mobile application-guided cardiac telerehabilitation in patients with atrial fibrillation: A randomised controlled trial. Clin Rehabil 2022 Jan 16;36(1):40-50. [CrossRef] [Medline]
    38. Guo Y, Guo J, Shi X, Yao Y, Sun Y, Xia Y, mAF-App II Trial investigators. Mobile health technology-supported atrial fibrillation screening and integrated care: a report from the mAFA-II trial Long-term Extension Cohort. Eur J Intern Med 2020 Dec;82:105-111 [FREE Full text] [CrossRef] [Medline]
    39. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med 2019 Nov 14;381(20):1909-1917. [CrossRef]
    40. Bashir MU, Bhagra A, Kapa S, McLeod CJ. Modulation of the autonomic nervous system through mind and body practices as a treatment for atrial fibrillation. Rev Cardiovasc Med 2019 Sep 30;20(3):129-137 [FREE Full text] [CrossRef] [Medline]
    41. Thrall G, Lip GY, Carroll D, Lane D. Depression, anxiety, and quality of life in patients with atrial fibrillation. Chest 2007 Oct;132(4):1259-1264. [CrossRef] [Medline]
    42. Guerrier G, Abdoul H, Jilet L, Rothschild P, Baillard C. Efficacy of a web app-based music intervention during cataract surgery: a randomized clinical trial. JAMA Ophthalmol 2021 Sep 01;139(9):1007-1013 [FREE Full text] [CrossRef] [Medline]
    43. Huberty J, Puzia ME, Green J, Vlisides-Henry RD, Larkey L, Irwin MR, et al. A mindfulness meditation mobile app improves depression and anxiety in adults with sleep disturbance: Analysis from a randomized controlled trial. Gen Hosp Psychiatry 2021 Nov;73:30-37 [FREE Full text] [CrossRef] [Medline]
    44. Chai PR, Schwartz E, Hasdianda MA, Azizoddin DR, Kikut A, Jambaulikar GD, et al. A brief music app to address pain in the emergency department: prospective study. J Med Internet Res 2020 May 20;22(5):e18537 [FREE Full text] [CrossRef] [Medline]
    45. Nørgaard MW, Werner A, Abrahamsen R, Larsen B, Darmer MR, Pedersen PU. Visualization and attentive behavior for pain reduction during radiofrequency ablation of atrial fibrillation. Pacing Clin Electrophysiol 2013 Feb 05;36(2):203-213. [CrossRef] [Medline]
    46. Nørgaard MW, Pedersen PU, Bjerrum M. Understanding how patients use visualization during ablation of atrial fibrillation in reducing their experience of pain, anxiety, consumption of pain medication and procedure length: Integrating quantitative and qualitative results. Appl Nurs Res 2018 Feb;39:229-240. [CrossRef] [Medline]
    47. Roxburgh T, Li A, Guenancia C, Pernollet P, Bouleti C, Alos B, et al. Virtual reality for sedation during atrial fibrillation ablation in clinical practice: observational study. J Med Internet Res 2021 May 27;23(5):e26349 [FREE Full text] [CrossRef] [Medline]
    48. Crivelli D, Fronda G, Venturella I, Balconi M. Supporting mindfulness practices with brain-sensing devices. Cognitive and electrophysiological evidences. Mindfulness 2018 Jun 19;10(2):301-311. [CrossRef]
    49. Balconi M, Fronda G, Crivelli D. Effects of technology-mediated mindfulness practice on stress: psychophysiological and self-report measures. Stress 2019 Mar 24;22(2):200-209. [CrossRef] [Medline]
    50. Barello S, Triberti S, Graffigna G, Libreri C, Serino S, Hibbard J, et al. eHealth for patient engagement: a systematic review. Front Psychol 2015;6:2013 [FREE Full text] [CrossRef] [Medline]
    51. Zeidan F, Vago D. Mindfulness meditation-based pain relief: a mechanistic account. Ann N Y Acad Sci 2016 Jun;1373(1):114-127 [FREE Full text] [CrossRef] [Medline]
    52. Tang Y, Hölzel BK, Posner MI. The neuroscience of mindfulness meditation. Nat Rev Neurosci 2015 Apr 18;16(4):213-225. [CrossRef] [Medline]
    53. Foji S, Tadayonfar MA, Mohsenpour M, Rakhshani MH. The study of the effect of guided imagery on pain, anxiety and some other hemodynamic factors in patients undergoing coronary angiography. Complement Ther Clin Pract 2015 May;21(2):119-123. [CrossRef] [Medline]
    54. Bayrak A, Sagiroglu G, Copuroglu E. Effects of preoperative anxiety on intraoperative hemodynamics and postoperative pain. J Coll Physicians Surg Pak 2019 Sep 01;29(9):868-873. [CrossRef] [Medline]
    55. Angioli R, De Cicco Nardone C, Plotti F, Cafà EV, Dugo N, Damiani P, et al. Use of music to reduce anxiety during office hysteroscopy: prospective randomized trial. J Minim Invasive Gynecol 2014 May;21(3):454-459. [CrossRef] [Medline]

    ‎
    AF: atrial fibrillation
    A-State: State Anxiety Inventory
    BCI: brain-computer interface
    BFI: Brief Fatigue Inventory
    CONSORT: Consolidated Standards of Reporting Trials
    EEG: electroencephalography
    RFCA: radiofrequency catheter ablation
    SpO2: peripheral oxygen saturation

    Edited by L Buis, G Eysenbach; submitted 06.12.22; peer-reviewed by X Gu, P Koopman, R Marshall, L Guo; comments to author 01.02.23; revised version received 28.03.23; accepted 13.04.23; published 03.05.23

    Copyright

    ©Ying He, Zhijie Tang, Guozhen Sun, Cheng Cai, Yao Wang, Gang Yang, ZhiPeng Bao. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 03.05.2023.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on https://mhealth.jmir.org/, as well as this copyright and license information must be included.