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.

本文在探讨如何解决数字治疗中患者面临的自我报告过多问题。通过引入游戏化因素和基于 PPG 信号的认知负荷检测技术，研究发现可以减轻患者的认知负荷，提高患者对数字治疗的参与度。

游戏化能减轻 mHealth 应用中自我报告的负担吗？使用智能手表数据的机器学习进行可行性研究以估计认知负荷

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

Understanding what makes people happy is a central topic in psychology. Prior
work has mostly focused on developing self-reporting assessment tools for
individuals and relies on experts to analyze the periodic reported assessments.
One of the goals of the analysis is to understand what actions are necessary to
encourage modifications in the behaviors of the individuals to improve their
overall well-being. In this paper, we outline a complementary approach; on the
assumption that the user journals her happy moments as short texts, a system
can analyze these texts and propose sustainable suggestions for the user that
may lead to an overall improvement in her well-being. We prototype one
necessary component of such a system, the Happiness Entailment Recognition
(HER) module, which takes as input a short text describing an event, a
candidate suggestion, and outputs a determination about whether the suggestion
is more likely to be good for this user based on the event described. This
component is implemented as a neural network model with two encoders, one for
the user input and one for the candidate actionable suggestion, with additional
layers to capture psychologically significant features in the happy moment and
suggestion.

通过分析用户记载的短文本，该论文提出了一种系统化的方法来帮助用户提高幸福感。该方法使用神经网络模型并重点分析短文本中与心理学相关的特征，最终输出有关行动建议是否适合该用户的决策，以期提高其整体的幸福感。