The effectiveness of digital treatments can be measured by requiring patients to self-report their mental and physical state through mobile applications. However, self-reporting can be overwhelming and may cause patients to disengage from the intervention. In order to address this issue, we conduct a feasibility study to explore the impact of gamification on the cognitive burden of self-reporting. Our approach involves the creation of a system to assess cognitive burden through the analysis of photoplethysmography (PPG) signals obtained from a smartwatch. The system is built by collecting PPG data during both cognitively demanding tasks and periods of rest. The obtained data is utilized to train a machine learning model to detect cognitive load (CL). Subsequently, we create two versions of health surveys: a gamified version and a traditional version. Our aim is to estimate the cognitive load experienced by participants while completing these surveys using their mobile devices. We find that CL detector performance can be enhanced via pre-training on stress detection tasks and requires capturing of a minimum 30 seconds of PPG signal to work adequately. For 10 out of 13 participants, a personalized cognitive load detector can achieve an F1 score above 0.7. We find no difference between the gamified and non-gamified mobile surveys in terms of time spent in the state of high cognitive load but participants prefer the gamified version. The average time spent on each question is 5.5 for gamified survey vs 6 seconds for the non-gamified version.

翻译：数字化治疗的有效性可通过要求患者通过移动应用自我报告其心理和身体状态来衡量。然而，自我报告可能带来过重负担，导致患者退出干预。为解决此问题，我们开展了一项可行性研究，探索游戏化对自我报告认知负担的影响。我们的方法包括构建一个系统，通过分析智能手表获取的光电容积脉搏波（PPG）信号来评估认知负担。该系统通过收集认知任务期间和休息期间的PPG数据构建，并利用所得数据训练机器学习模型以检测认知负荷（CL）。随后，我们创建了两个版本的健康调查问卷：游戏化版本和传统版本。目的是通过移动设备估算参与者在完成问卷时的认知负荷。研究发现，通过在压力检测任务上进行预训练并捕获至少30秒的PPG信号，可提升认知负荷检测器的性能。对13名参与者中的10名，个性化认知负荷检测器的F1分数可达0.7以上。游戏化与非游戏化移动问卷在高认知负荷状态下的耗时未见差异，但参与者更偏好游戏化版本。游戏化问卷的平均每题答题时间为5.5秒，非游戏化版本为6秒。