Breathing as an Input Modality in a Gameful Breathing Training App (Breeze 2): Development and Evaluation Study
Open accessAbstract
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.
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Tempo breathing asks people to inhale for 4 s, hold their breath for 1 s, and exhale for 5 s.
“Tempo breathing asks people to inhale for 4 s, hold their breath for 1 s, and exhale for 5 s (Lukic et al., 2022).”
From Frontal Electroencephalography Asymmetry and Desynchronized Functional Connectivity Associated with Long-Term and Short-Term Breathing Training (2026) · cites this paper as Lukic et al., 2022
The Methods (Design and Implementation; page not provided) specify 4 seconds of inhalation, a 1-second pause, and 5 seconds of exhalation at Breeze 2’s standard setting of 6 breaths per minute. The paper does not identify this as “Tempo breathing,” and the durations change when users select a different breathing rate. (LLM, conf=0.85)
Experimental — accuracy not yet measuredBreathing training includes different styles, such as mindful breathing, tempo breathing, and counting breath cycles.
“Breathing training includes different styles, such as mindful breathing (Fjorback et al., 2011), tempo breathing (Lukic et al., 2022), and counting breath cycles (Shuai et al., 2020).”
From Frontal Electroencephalography Asymmetry and Desynchronized Functional Connectivity Associated with Long-Term and Short-Term Breathing Training (2026) · cites this paper as Lukic et al., 2022
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Self-citationSame Breath, Different Device: Comparing VR and Smartphone Biofeedback Breathing Training
Ackermann, L. J., Galliker, H., Lukic, Y. X., & Kowatsch, T. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798443
Self-citationSame breath, different device : comparing VR and smartphone biofeedback breathing training
Ackermann, L. J., Galliker, H., Lukic, Y. X., & Kowatsch, T. (2026) · Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) · doi:10.21256/zhaw-36401
Self-citationRaichura, P., & Hadjikhani, N. K. (2026) · doi:10.2196/preprints.97884
ArthroTour: A Serious Game to Educate About Osteoarthritis
Henning, G., Pirker, J., Plecher, D. A., & Eichhorn, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-30405-6_5
- Authors
- Lukic, Y. X., Teepe, G. W., Fleisch, E., & Kowatsch, T.
- Affiliations
- ETH Zurich (CH)
- University of St.Gallen (CH)
- University of Zurich (CH)
- Corresponding author
- Lukic, Yanick Xavier
- Venue
- JMIR Serious Games
- Publisher
- JMIR Publications Inc.
- Published
- 2022-08-16
- Volume
- 10
- Issue
- 3
- Pages
- e39186
- ISSN
- 2291-9279 (ISSN-L)
- PMC
- PMC9428773
- OpenAlex
- W4286524748
- Cited by
- 32
- References
- 59
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 10 · 2023: 5 · 2022: 1
- FWCI
- 5.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Serious Games (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection
- Zurich Open Repository and Archive (University of Zurich)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Keywords
- gamified learning, breathing exercises, slow-paced breathing, deep transfer learning, user engagement, perceived effectiveness, smartphone microphone, headphones, audio-based interaction, sex differences
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