Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load

• URL: http://arxiv.org/abs/2302.03616v3
• Date: Thu, 21 Dec 2023 13:06:12 GMT
• Title: Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load
• Authors: Michal K. Grzeszczyk and Paulina Adamczyk and Sylwia Marek and Ryszard Pr\k{e}cikowski and Maciej Ku\'s and M. Patrycja Lelujko and Rosmary Blanco and Tomasz Trzci\'nski and Arkadiusz Sitek and Maciej Malawski and Aneta Lisowska
• Abstract summary: The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications. We conduct a study to explore the impact of. gamified on self-reporting on cognitive load.
• Score: 8.48141991380649
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals. The data from 11 participants is utilized to train a machine learning model to detect CL. Subsequently, we create two versions of surveys: a gamified and a traditional one. We estimate the CL experienced by other participants (13) while completing surveys. We find that CL detector performance can be enhanced via pre-training on stress detection tasks. For 10 out of 13 participants, a personalized CL detector can achieve an F1 score above 0.7. We find no difference between the gamified and non-gamified surveys in terms of CL but participants prefer the gamified version.

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