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:数字治疗的有效性可以通过要求患者通过移动应用程序自我报告其精神和身体状况来衡量数字治疗的效果。然而,自我报告可能压倒性,可能导致患者脱离干预。为了解决这一问题,我们进行了可行性研究,以探讨拼写对自我报告认知负担的影响。我们的方法是通过分析从智能观察获得的光膜成像仪信号,建立一个评估认知负担的系统。该系统是通过在认知要求很高的任务和休息期间收集PPPG数据而建立的。获得的数据用于培训机器学习模型以检测认知负荷(CL),随后,我们制作了两种健康调查版本:一个拼写版本和一个传统版本。我们的目的是评估参与者在使用移动设备完成这些调查时所经历的认知负荷。我们发现CL探测器的性能可以通过压力检测任务培训来提高,并且需要将至少30秒钟的PPPGG信号捕捉到充分的工作。在13名参与者中,个人化的认知荷负载检测器检测器可以达到超过0.7秒钟的F1分数。我们的目标是在每次已耗时进行移动式平均时间调查时,我们没有发现GAM之间的差异。