Mindfulness Training and Respiration Biosignal Feedback - Study 1
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 80
- 试验地点
- 2
- 主要终点
- Optimization of deep learning algorithms that correlate changes in respiration dynamics during meditation training to changes in mindfulness skills from pre-training to post-training
研究概览
简要总结
The goal of this research is to develop a new breathing feature on the meditation app, Equa, to help young adults who are distressed, understand their physiological responses and mindfulness skill development during meditation.
Our main aims are to build an algorithm that can use physiologic signals to:
- Give feedback about how participant physiology is changing during guided lessons on the meditation app, Equa
- Measure how much participant mindfulness skills are improving
Participants will:
- Complete a survey about demographics, their thoughts and feelings before and after the mindfulness meditation program
- Complete 14 smartphone guided mindfulness meditation training units while physiological measures are being recorded
- Complete a few brief questionnaires before and after mindfulness practices to understand potential changes in their mindfulness skills
详细描述
Investigators will recruit young adults to participate in a study to examine the effectiveness of respiration dynamics during meditation through phone motion data and microphones within headphones. Interested participants who contact us will be screened on study inclusion/exclusion criteria: (1) aged 18-30 years, (3]2) interested in coming on site to complete 14 smartphone guided mindfulness meditation training units, (4) willing to wear physiological monitoring equipment and provide ratings of their training experience, (5) Not currently pregnant and (6) no current or previous diagnosis of psychosis or schizophrenia
Participants are told they are going to participate in a study that focuses on monitoring physiological responses during meditation. At the start of the study, participants will complete questions via an online survey focused on demographics, prior meditation experience, their thoughts and feelings as these may be informative to participants' meditation experience.
Participants will complete a few brief questionnaires before and after each mindfulness practices to understand potential changes in mindfulness . Participant physiological data will be recorded (E.g., heart rate) via smartphones and headphones to track physiologic dynamics. Additionally, the sensory shirt, made by Hexoskin Smart Sensors; AI, will also continuously measure physiologics via two inductive plethysmography (RIP) sensors. The Hexoskin shirt also tracks motion via a three-axis accelerometer. These measures will enable investigators to better understand mindfulness measures during meditation.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Basic Science
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 30 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Ages 18-30 years of Age
- •Fluent in English
- •Psychological distress
- •Willing to participate in guided meditation or stress management training.
- •Willing and able to wear earbud headphones and a shirt which uses sensors to track motion and physiological measures.
- •Willing to provide ratings on their training experience
排除标准
- •Currently pregnant
- •Current or previous diagnosis with psychosis or schizophrenia
结局指标
主要结局
Optimization of deep learning algorithms that correlate changes in respiration dynamics during meditation training to changes in mindfulness skills from pre-training to post-training
时间窗: Same day, change from pre-mindfulness meditation training to post-mindfulness meditation training
Prediction accuracy of greater than 90%. There are three distinct mindfulness skills that will be measured before and after trainings - Concentration, Sensory Clarity, and Equanimity. Participants will respond to a likert scale from one to five. One is equivalent to a poor perceived level of skill and five indicates an excellent perceived level of skill.
次要结局
- User satisfaction with the mindfulness meditation app, Equa as assessed by the Mobile App Rating Scale at post treatment(At the end of treatment session at up to week 4)
- Change from Baseline in total anxiety as assessed by the General Anxiety Disorder, 7-item questionnaire at post treatment(From enrollment to the end of treatment at up to week 4)
- Change from Baseline in total depression as assessed by the Patient Health Questionnaire-9 at post treatment(From enrollment to the end of treatment at up to week 4)
- Change from Baseline in total affect as assessed by the Positive and Negative Affect Scale at post treatment(from enrollment to the end of treatment at up to week 4)
- Change from Baseline in mean state mindfulness as assessed by the Mindful Attention Awareness Scale - state at post treatment(From enrollment to the end of treatment at up to week 4)
- Change from Baseline in total perceived stress as assessed by the Perceived Stress Scale at post treatment(From enrollment to the end of treatment at up to week 4)
- Change from Baseline in total social well-being as assessed by the Satisfaction with Life Scale at post treatment(From enrollment to the end of treatment at up to week 4)
- Change from Baseline in total loneliness as assessed by the UCLA Loneliness scale at post treatment(From enrollment to the end of treatment at up to week 4)
- User satisfaction with the mindfulness meditation app, Equa as assessed by the Systems User Satisfaction scale at post treatment(End of treatment session at up to week 4)
研究者
David Creswell
Co-Founder, Chief Science Officer
Equa Health