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

TL;DR

This study explores whether gamification can reduce the burden of self-reporting in mHealth apps by using machine learning to estimate cognitive load from smartwatch data, finding no difference in load but higher user preference for gamified surveys.

Contribution

The paper introduces a machine learning-based system to assess cognitive load from smartwatch data and evaluates its impact on gamified versus traditional surveys in mHealth.

Findings

Cognitive load detector performance improves with pre-training on stress detection.

Personalized detectors achieve F1 scores above 0.7 for most participants.

Participants prefer gamified surveys despite no difference in cognitive load.

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.