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Gamified Surveys and Cognitive Load Detection in mHealth: Conclusion, Acknowledgment, and References

by Gamifications FTW PublicationsOctober 16th, 2024

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This study explores gamified mHealth surveys and machine learning-based cognitive load detection, aiming to improve patient engagement and survey completion.

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Authors:

(1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD.

Table of Links

Abstract and Introduction

Related Work

Methods

Results and Discussion

Limitations

Conclusion, Acknowledgment, and References

Conclusion

We developed a cognitive load detector and two versions of mobile surveys to investigate if gamification can reduce the burden of self-reporting. We have found no impact of the addition of simple game elements such as: progress tracking, avatar, rewards on the amount of time spent in high cognitive load or stress during filling in the surveys. However, this feasibility study yields practical learning related to cognitive load model training, such as: 1) Performance of CNN-based cognitive load detector from PPG signal is boosted via transfer learning on stress detection task. 2) There is a link between the model performance on the source and target task. 3) The minimum length of signal for cognitive load classification is 30 seconds but the addition of extra temporal context can further boost the detection. 4) Matching models to the participants using a small calibration dataset can facilitate finding a detector that can reliably distinguish between high and low cognitive load for each individual.

Acknowledgment

This work is supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement Sano No 857533 and the International Research Agendas programme of the Foundation for Polish Science, co-financed by the European Union under the European Regional Development Fund.

References

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