Mindfulness Meditation and Respiration: Accelerometer-Based Respiration Rate and Mindfulness Progress Estimation to Enhance App Engagement and Mindfulness Skills
Mohammad Nur Hossain Khan, David Creswell, Jordan Albert, Patrick O'Connell, Shawn Fallon, Mathew Polowitz, Xuhai "orson" Xu, Bashima Islam
arXiv Preprint Archive July 23, 2025 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | User study with experimental and control groups |
|---|---|
| Population | Participants in mindfulness sessions |
| Intervention | Respiration biosignal feedback |
| Topics | Meditation |
| Keywords | Cs.hc Cs.lg Digital mindfulness Mindfulness apps Meditation apps E-mindfulness Digital wellness Mobile mindfulness Real-time biofeedback Breathing feedback Physiological tracking Sensor-based monitoring Accelerometer tracking Breath monitoring Skill assessment Mindfulness skill estimation Progress tracking User progress monitoring Performance evaluation Personalized training Human-computer interaction Hci Usability User experience ux Interactive systems Digital health interfaces User-centered design Machine learning Ml Artificial intelligence ai Data analysis Predictive modeling Pattern recognition Algorithm development |
| Key findings | Respiration biosignal feedback from a smartphone's accelerometer enhances system usability and enables tracking of mindfulness skill progression. |
Abstract
Mindfulness training is widely recognized for its benefits in reducing depression, anxiety, and loneliness. With the rise of smartphone-based mindfulness apps, digital meditation has become more accessible, but sustaining long-term user engagement remains a challenge. This paper explores whether respiration biosignal feedback and mindfulness skill estimation enhance system usability and skill development. We develop a smartphone's accelerometer-based respiration tracking algorithm, eliminating the need for additional wearables. Unlike existing methods, our approach accurately captures slow breathing patterns typical of mindfulness meditation. Additionally, we introduce the first quantitative framework to estimate mindfulness skills-concentration, sensory clarity, and equanimity-based on accelerometer-derived respiration data. We develop and test our algorithms on 261 mindfulness sessions in both controlled and real-world settings. A user study comparing an experimental group receiving biosignal feedback with a control group using a standard app shows that respiration feedback enhances system usability. Our respiration tracking model achieves a mean absolute error (MAE) of 1.6 breaths per minute, closely aligning with ground truth data, while our mindfulness skill estimation attains F1 scores of 80-84% in tracking skill progression. By integrating respiration tracking and mindfulness estimation into a commercial app, we demonstrate the potential of smartphone sensors to enhance digital mindfulness training.
Comparable studies
Other experimental studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| The benefits of being present: Mindfulness and its role in psychological well-being. Cancer patients (clinical intervention study) | 2003 | Mixed methods: scale development, correlational, quasi-experimental, laboratory, experience-sampling, and clinical intervention study | |
| Loving-kindness meditation increases social connectedness. | 2008 | Controlled laboratory experiment | |
| Mindfulness and mind-wandering: Finding convergence through opposing constructs. | 2012 | Two studies: correlational study and experimental study | |
| Mental Training Enhances Attentional Stability: Neural and Behavioral Evidence | 2009 | Experimental study | |
| Mindfulness meditation and substance use in an incarcerated population. Incarcerated population | 2006 | Quasi-experimental with treatment-as-usual control condition |