Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load
AMIA Annu Symp Proc. 2024 Jan 11;2023:389-396. eCollection 2023.ABSTRACTThe 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.PMID:38222421 | PMC:PMC10785949
Source: AMIA Annual Symposium Proceedings - Category: Bioinformatics Authors: Michal K Grzeszczyk Paulina Adamczyk Sylwia Marek Ryszard Pr ęcikowski Maciej Ku ś M Patrycja Lelujko Rosmary Blanco Tomasz Trzci ński Arkadiusz Sitek Maciej Malawski Aneta Lisowska Source Type: research
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