Calm a Mobile based Deep Breathing Game with Biofeedback

Agrawal, Vasundhara; Naik, Varnika; Duggirala, Mayuri; Athavale, Sandeep

doi:10.1145/3383668.3419876

Cited by 11 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One innovative strategy that’s gaining traction is the incorporation of gamification into deep breathing interventions. By gamifying deep breathing exercises, these interventions aim not only to harness the therapeutic advantages of controlled respiration but also to elevate user commitment, thereby improving the overall effectiveness of the intervention ( 15 , 16 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Building upon the foundation set by previous studies, such as “Calm: Blow away your Stress” and “Breeze”, our research proposes an optimal design for gamifying deep breathing interventions that target relaxation. For instance, the mobile application “Calm: Blow away your Stress” ( 15 ) invites users to dissipate twelve clouds using their breath through the microphone. The application’s animated clouds and breath-based feedback stimulate user curiosity and engagement.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Deep breathing in your hands: designing and assessing a DTx mobile app

Jeong,

Yoo,

Goh

et al. 2024

Front. Digit. Health

View full text Add to dashboard Cite

Digital Therapeutics (DTx) are experiencing rapid advancements within mobile and mental healthcare sectors, with their ubiquity and enhanced accessibility setting them apart as uniquely effective solutions. In this evolving context, our research focuses on deep breathing, a vital technique in mental health management, aiming to optimize its application in DTx mobile platforms. Based on well-founded theories, we introduced a gamified and affordance-driven design, facilitating intuitive breath control. To enhance user engagement, we deployed the Mel Frequency Cepstral Coefficient (MFCC)-driven personalized machine learning method for accurate biofeedback visualization. To assess our design, we enlisted 70 participants, segregating them into a control and an intervention group. We evaluated Heart Rate Variability (HRV) metrics and collated user experience feedback. A key finding of our research is the stabilization of the Standard Deviation of the NN Interval (SDNN) within Heart Rate Variability (HRV), which is critical for stress reduction and overall health improvement. Our intervention group observed a pronounced stabilization in SDNN, indicating significant stress alleviation compared to the control group. This finding underscores the practical impact of our DTx solution in managing stress and promoting mental health. Furthermore, in the assessment of our intervention cohort, we observed a significant increase in perceived enjoyment, with a notable 22% higher score and 10.69% increase in positive attitudes toward the application compared to the control group. These metrics underscore our DTx solution’s effectiveness in improving user engagement and fostering a positive disposition toward digital therapeutic efficacy. Although current technology poses challenges in seamlessly incorporating machine learning into mobile platforms, our model demonstrated superior effectiveness and user experience compared to existing solutions. We believe this result demonstrates the potential of our user-centric machine learning techniques, such as gamified and affordance-based approaches with MFCC, which could contribute significantly to the field of mobile mental healthcare.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Deep breathing in your hands: designing and assessing a DTx mobile app

Jeong,

Yoo,

Goh

et al. 2024

Front. Digit. Health

View full text Add to dashboard Cite

show abstract

“…The problem of nonadherence and lacking engagement is present in various domains, and different works hypothesize gamification as a potential solution [ 18 , 19 ]. In addition, for breathing training, various mobile [ 20 - 22 ], desktop [ 23 ], and virtual reality [ 24 , 25 ] applications that use some gameful elements have been conceptualized and developed. However, many apps are not interactive and do not include biofeedback during training, which is surprising as research indicates increased effectiveness of breathing training when biofeedback is used [ 26 - 29 ].…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, breathing-based biofeedback can be instantaneous and is the direct signal that the guidance in breathing training apps tries to change to then affect HRV. However, approaches using breathing-based biofeedback are so far limited to breathing training in controlled environments [ 21 ] and early prototypes [ 22 ]. One of the first apps to go in this direction was Breeze (Centre for Digital Health Interventions) [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Breathing as an Input Modality in a Gameful Breathing Training App (Breeze 2): Development and Evaluation Study

Lukic¹,

Teepe²,

Fleisch³

et al. 2022

JMIR Serious Games

View full text Add to dashboard Cite

Background Slow-paced breathing training can have positive effects on physiological and psychological well-being. Unfortunately, use statistics indicate that adherence to breathing training apps is low. Recent work suggests that gameful breathing training may help overcome this challenge. Objective This study aimed to introduce and evaluate the gameful breathing training app Breeze 2 and its novel real-time breathing detection algorithm that enables the interactive components of the app. Methods We developed the breathing detection algorithm by using deep transfer learning to detect inhalation, exhalation, and nonbreathing sounds (including silence). An additional heuristic prolongs detected exhalations to stabilize the algorithm’s predictions. We evaluated Breeze 2 with 30 participants (women: n=14, 47%; age: mean 29.77, SD 7.33 years). Participants performed breathing training with Breeze 2 in 2 sessions with and without headphones. They answered questions regarding user engagement (User Engagement Scale Short Form [UES-SF]), perceived effectiveness (PE), perceived relaxation effectiveness, and perceived breathing detection accuracy. We used Wilcoxon signed-rank tests to compare the UES-SF, PE, and perceived relaxation effectiveness scores with neutral scores. Furthermore, we correlated perceived breathing detection accuracy with actual multi-class balanced accuracy to determine whether participants could perceive the actual breathing detection performance. We also conducted a repeated-measure ANOVA to investigate breathing detection differences in balanced accuracy with and without the heuristic and when classifying data captured from headphones and smartphone microphones. The analysis controlled for potential between-subject effects of the participants’ sex. Results Our results show scores that were significantly higher than neutral scores for the UES-SF (W=459; P<.001), PE (W=465; P<.001), and perceived relaxation effectiveness (W=358; P<.001). Perceived breathing detection accuracy correlated significantly with the actual multi-class balanced accuracy (r=0.51; P<.001). Furthermore, we found that the heuristic significantly improved the breathing detection balanced accuracy (F1,25=6.23; P=.02) and that detection performed better on data captured from smartphone microphones than than on data from headphones (F1,25=17.61; P<.001). We did not observe any significant between-subject effects of sex. Breathing detection without the heuristic reached a multi-class balanced accuracy of 74% on the collected audio recordings. Conclusions Most participants (28/30, 93%) perceived Breeze 2 as engaging and effective. Furthermore, breathing detection worked well for most participants, as indicated by the perceived detection accuracy and actual detection accuracy. In future work, we aim to use the collected breathing sounds to improve breathing detection with regard to its stability and performance. We also plan to use Breeze 2 as an intervention tool in various studies targeting the prevention and management of noncommunicable diseases.

show abstract

“…There are limited tools, toys, and digital games that are specially designed for specific child populations (e.g., ADHD and ASD) (Javed et al, 2018;Pina et al, 2014). Other tools and playthings related to practising deep breathing for children and adults are digitised (Agrawal et al, 2020;Hu et al, 2021;Tabor et al, 2020;Tu et al, 2020). However, experts' interviews findings highlight that children at this age need to explore and play physically through their sensory inputs.…”

Section: Deep Breathing For Anger Regulation Learningmentioning

confidence: 99%

Research Through Design for Anger Co-Emotion Regulation Learning in Young Children: A Soft Toy Design for Deep Breathing

Iskanderani

View full text Add to dashboard Cite

For young children to develop emotional competence skills, they need their parents' support. However, techniques to support parents are critiqued for being resource-intensive, time-consuming, and lacking a focus on the home environment. This research aims to design playful interactions to help parents to teach neurotypical children ages 2-3 effective anger regulation strategies.For this study, following a pragmatic paradigm, a human-centred design methodology was selected to understand the needs of parents and their children. A range of qualitative methods, such as semi-structured interviews, were used to determine how to incorporate play into teaching young children emotion regulation skills. Research through design approach was used to experiment and generate design concepts and to evaluate with focus groups. Parents and experts (including child psychologists and occupational therapists) then tested the prototype through a usability study to validate the anticipated learning through play.Initial findings indicate that some parents' emotion regulation practises may encourage children to avoid their emotions. The study’s design criteria indicate that playful interaction should adopt a range of techniques, including metaphors, sensory regulation strategies, parent involvement, and physical objects. A scheme to teach young children to identify and manage emotions through playful interactions was therefore designed. This scheme incorporated facial expressions and physiological components, along with age-appropriate metaphors. Five strategies were constructed to help future designers develop playful interactions. The design strategies consider any emotion by looking for ways to express it facially and physically, to include a strategy for regulating the senses, to be broken down into simple steps, and to include sensory inputs that help the child engage in the play.This study provides a unique and innovative contribution to playful learning, emotion regulation research, and design. It follows recent findings confirming the importance of parent's involvement in their children’s emotion regulation learning. It also identifies key strategies for teaching emotional competence skills. The findings of this study have the potential to inform the design of future educational and playful interactions for emotional competence skills, as well as expand their application in the field of interaction design and children.

show abstract

Calm a Mobile based Deep Breathing Game with Biofeedback

Cited by 11 publications

References 32 publications

Deep breathing in your hands: designing and assessing a DTx mobile app

Deep breathing in your hands: designing and assessing a DTx mobile app

Breathing as an Input Modality in a Gameful Breathing Training App (Breeze 2): Development and Evaluation Study

Research Through Design for Anger Co-Emotion Regulation Learning in Young Children: A Soft Toy Design for Deep Breathing

Contact Info

Product

Resources

About