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.

翻译：数字疗法的有效性可通过要求患者在应用中自我报告状态来衡量，但这种方式可能造成患者负担过重并导致参与度下降。我们开展了一项研究，探索游戏化对自我报告环节的影响。本研究通过分析光电容积脉搏波（PPG）信号构建认知负荷（CL）评估系统。利用11名参与者的数据训练机器学习模型以检测认知负荷，随后设计了两类问卷：游戏化版本与传统版本。我们评估了另外13名参与者在完成问卷时的认知负荷水平。研究发现，通过压力检测任务的预训练可提升认知负荷检测器的性能。在13名参与者中，10人的个性化认知负荷检测器F1分数超过0.7。游戏化与非游戏化问卷在认知负荷指标上无显著差异，但参与者更偏好游戏化版本。