Breast Cancer, Chemotherapy, Mindfulness, Virtual Reality
Conditions
Keywords
breast cancer, Chemotherapy, Virtual Reality, Mindfulness
Brief summary
Breast cancer patients undergoing chemotherapy often face significant physical and emotional symptoms, with stress contributing to symptom severity. This study investigates the effectiveness of a mindfulness-based virtual reality (VR) intervention in reducing stress and symptom severity. A total of 60 participants will be stratified and randomized into three groups: mindfulness-based VR, mindfulness-based audio, and control. Data will be analyzed using generalized estimating equations and machine learning. The goal is to improve understanding and quality of care for cancer patients by evaluating the potential benefits of mindfulness-based VR interventions.
Detailed description
Background: Breast cancer patients undergoing chemotherapy commonly experience physical and emotional symptoms, with approximately 90 percent of patients being affected. Perceived stress is one of the related factors that can contribute to symptom severity. Inadequate control of symptoms can have significant negative consequences on patient's quality of life, treatment adherence, disease prognosis, and increase the number of doctor visits, and overall treatment outcomes. The application of virtual reality in improving symptoms and perceived stress in breast cancer patients undergoing chemotherapy is an area of ongoing research, and the findings from existing studies have been inconsistent. Additionally, the incorporation of mindfulness techniques within VR interventions for this specific population has been limited. Objective: This study aims to explore the effectiveness of mindfulness-based virtual reality on the stress perception and symptom severity of breast cancer patients undergoing chemotherapy. Methods: Stratified block randomization will be used to assign 20 patients each to the mindfulness based virtul reality group, the mindfulness based audio practice group, and the control group, totaling 60 participants. Data analysis will be conducted using the generalized estimating equation and machine learning. Expected results: To understand the effectiveness of mindfulness-based virtual reality on the stress perception and the symptoms severity of chemotherapy in breast cancer patients, in order to serve as a reference for improving the quality of care for cancer patients in the future.
Interventions
BEHAVIORALmindfulness based virtual reality
mindfulness based virtual reality
BEHAVIORALmindfulness based audio practice
mindfulness based audio practice
Sponsors
Hsin Yi Lu
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
SINGLE (Outcomes Assessor)
Masking description
The Outcomes Assessor will perform physiological data extraction and questionnaire result analysis without knowledge of the group assignments of the data being analyzed.
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age above 18 years. * Diagnosed with breast cancer stages I to III, with diagnostic codes C50, C79.81, C84.7A, D03.52, D05, D24, D48, or D49; there are no restrictions on tumor type, receptor subtype, or whether breast tumor removal surgery has been performed. * Undergoing the first inpatient preoperative or postoperative chemotherapy, without restrictions on the type of medication or treatment cycles. * Able to communicate in Mandarin and literate; without cognitive impairments, psychiatric disorders, motion sickness, epilepsy, or a history of drug or alcohol addiction. * No prior experience with mindfulness-based interventions. Understanding of the research procedures, agreement to participate, and signing of the informed consent form; owning a smartphone capable of installing the required research applications and able to operate independently.
Exclusion criteria
* individuals with blindness or visual impairments that preclude the identification of on-screen visuals * individuals with auditory impairments preventing the recognition of sounds through headphones.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| stress perception | Pre- and post-intervention assessments will be conducted from the first inpatient chemotherapy session to the fifth inpatient chemotherapy session (through study completion, an average of 15 weeks to 20 weeks) | The Revised Newly Diagnosed Breast Cancer Stress Scale (NDBCSS-R) will be used: This scale consists of 17 items, divided into four parts: Negative Perception (3 items), Threat (4 items), Unpredictability (5 items), and Facing Challenge (4 items). Scoring is based on a 4-point Likert scale, with 3 indicating strong agreement and 0 indicating disagreement. Higher scores indicate a greater level of perceived stress. Items 1, 3, 5, 11, 13, and 15 are reverse-scored. |
| symptom severity | Pre- and post-intervention assessments will be conducted from the first inpatient chemotherapy session to the fifth inpatient chemotherapy session (through study completion, an average of 15 weeks to 20 weeks) | The Memorial Symptom Assessment Scale (MSAS) will be used: It consists of 32 items. First, participants indicate the presence or absence of symptoms. If symptoms are present, the frequency, severity, and distress of 24 symptoms, and the severity and distress of 8 symptoms, are evaluated. Frequency and severity are assessed using a 4-point scale, with 1 indicating rarely or mild and 4 indicating almost constantly or very severe. If there are no symptoms, the item is scored as 0. Distress is assessed using a 5-point scale, with 0 indicating not at all and 4 indicating very much. |
| Mindfulness efficacy | Pre- and post-intervention assessments will be conducted from the first inpatient chemotherapy session to the fifth inpatient chemotherapy session (through study completion, an average of 15 weeks to 20 weeks) | The Cognitive and Affective Mindfulness Scale-Revised (CAMS-R) will be used: It consists of 10 items, rated on a 4-point scale, with 1 indicating almost never and 4 indicating almost always. Higher scores indicate a higher level of mindfulness. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Electrodermal Activity | Pre- and post-intervention assessments will be conducted from the first inpatient chemotherapy session to the fifth inpatient chemotherapy session (through study completion, an average of 15 weeks to 20 weeks) | Skin conductance monitoring electrodes from the Thought Technology five-channel biofeedback system kit will be used. Using the BioSPPy package in Python, we first apply a filter for noise reduction. Then, we detect peaks and extract features. |
| heart rate variability | Pre- and post-intervention assessments will be conducted from the first inpatient chemotherapy session to the fifth inpatient chemotherapy session (through study completion, an average of 15 weeks to 20 weeks) | The three-lead electrocardiography (ECG) from the Thought Technology five-channel biofeedback system kit will be used as the measurement tool. Using the NeuroKit2 package in Python, we aim to extract R-peaks from time-series data. Initially, we apply a 60 Hz notch filter to remove powerline noise, followed by a bandpass filter with a frequency range of 0.05 to 100 Hz. To enhance the QRS complex and make the R-peaks more prominent within the signal, we use a dynamic threshold setting based on the signal amplitude from the preceding 15 seconds. Subsequently, we detect the peaks, verify, and correct them. Finally, we calculate the HRV features. |
| Salivary cortisol concentration | Pre- and post-intervention assessments will be conducted from the first inpatient chemotherapy session to the fifth inpatient chemotherapy session (through study completion, an average of 15 weeks to 20 weeks) | The enzyme-linked immunosorbent assay (ELISA) will be used for detection. This method is based on the specific binding of antibodies to antigens, combined with a chromogenic substrate reaction. The color intensity is used to quantify the concentration of the specific target analyte in the sample. |
| Respiration pattern variability | Pre- and post-intervention assessments will be conducted from the first inpatient chemotherapy session to the fifth inpatient chemotherapy session (through study completion, an average of 15 weeks to 20 weeks) | Respiration belts from the Thought Technology five-channel biofeedback system kit will be used to measure changes in chest volume during breathing. Using the NeuroKit2 package in Python, we employ Fast Fourier Transform (FFT) to convert the time-series data to the frequency domain. We set the frequency range to 0.2 to 0.33 Hz to correspond to a normal average breathing rate of 12 to 20 breaths per minute, thereby removing irrelevant noise. The difference method is used to calculate and establish the starting points of inhalation and exhalation, allowing for the calculation of breathing rate and relative breathing depth. A moving average method is then applied to provide a stable mean representation of the breathing rate, eliminating transient anomalies. These processed data are subsequently used to calculate RRV features. |
| Electroencephalogram | Pre- and post-intervention assessments will be conducted from the first inpatient chemotherapy session to the fifth inpatient chemotherapy session (through study completion, an average of 15 weeks to 20 weeks) | The OpenBCI Ganglion Board will serve as the development platform, with the chip and wet electrodes integrated into a virtual reality headset for user convenience. Bluetooth connectivity will interface with the Pico headset, transmitting EEG signals to the software. A four-channel EEG setup, with electrodes placed according to the international 10-20 system, will be employed. Using the NeuroKit2 package in Python, we preprocess EEG data by initially filtering to remove 60 Hz noise and retain 2 to 30 Hz signals. We then convert the signals to the time-frequency domain using Morlet Wavelet, capturing features across different frequency bands. The power spectral density of each channel is segmented into δ, θ, α, β, and γ bands, and the average power in each band is calculated to analyze and compare brainwave activity intensity. |
Countries
Taiwan
Contacts
Primary ContactHsin-Yi Lu
Outcome results
None listed