Kowatsch, TobiasTobiasKowatschShih, IrisIrisShihLukic, Yanick XavierYanick XavierLukicKeller, Olivia ClareOlivia ClareKellerHeldt, KatrinKatrinHeldtDurrer, DominiqueDominiqueDurrerStasinaki, AikateriniAikateriniStasinakiBüchter, DirkDirkBüchterBrogle, BjörnBjörnBrogleFarpour-Lambert, NathalieNathalieFarpour-Lambertl'Allemand, DagmarDagmarl'Allemand2023-04-132023-04-132021-04-14https://www.alexandria.unisg.ch/handle/20.500.14171/110486Effective interventions for the prevention and treatment of child and adolescent obesity play an important role in reducing the global health and economic burden of non-communicable diseases. Although multi-component interventions targeting various health behaviors are deemed promising, evidence for their effectiveness is still limited. Self-regulation seems to be a relevant working mechanism in this regard. Therefore, we propose a playful, smartphone-based self-regulation training that also utilizes the health benefits of a slow-paced breathing exercise. The mobile app uses the microphone of the smartphone to detect breathing sounds (e.g. inhalation, exhalation) and translates these sounds into a visual biofeedback on the smartphone screen. The design and evaluation of a very first prototype is described in this interdisciplinary work of obesity experts, clinical psychologists, young patients, and computer scientists. The apps' breathing detection module uses a random forest tree for quasi real-time classification of the incoming audio samples and biofeedback generation. A study with 11 obese children and adolescents was conducted to assess the prototype. Results indicate overall positive evaluations and suggestions for improvement. Implications and limitations are discussed, and an outlook on future work is provided.enBiofeedbackdigital health interventionself-regulationbreathing trainingobesityconference paper