As mobile health (mHealth) studies become increasingly productive due to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. We examine the factors that affect participants' responsiveness to ecological momentary assessments (EMA) in a 10 day wearable and EMA based affect sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the two constructs. We collected the data from 45 healthy participants wearing two devices measuring electrodermal activity, acceleration, electrocardiography, and skin temperature while answering 10 EMAs a day containing questions related to perceived mood. Due to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during a response. Therefore, we utilized unsupervised and supervised learning methods to infer affect when a participant did not respond. Based on our findings we showed that using a receptivity model to trigger EMAs will decrease the reported negative affect by more than 3 points or 0.29 standard deviation using our psychological instrument scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of a mHealth study, particularly those studies that employ a learning algorithm to trigger EMAs. Therefore, we propose a smart trigger that promotes EMA and JITI receptivity without influencing affect during sampled time points as future work.

翻译：随着可穿戴和移动传感器技术的进步，移动健康研究日益高效，我们监测和建模人类行为的能力将受到参与者接收性的制约。许多健康构念依赖于主观反应，若无此类反应，研究者几乎无法获得伴随不断增长的生物行为数据的真实基准。我们在一项为期10天的基于可穿戴设备和生态瞬时评估的情感感知移动健康研究中，考察了影响参与者对生态瞬时评估响应能力的因素。我们研究了指示接收性和情感的生理关系，同时分析这两个构念之间的交互作用。我们收集了45名健康参与者的数据，他们佩戴两种设备测量皮肤电活动、加速度、心电图和皮肤温度，同时每天回答10个涉及感知情绪的生态瞬时评估问题。由于构念的性质，我们仅在参与者响应时才能获得情感和接收性的真实基准测量。因此，我们利用无监督和监督学习方法推断参与者未响应时的情感。基于研究发现，我们表明使用接收性模型触发生态瞬时评估，可将在心理量表（评分范围13至91）上报告的负面情感降低超过3分或0.29个标准差。研究结果还显示，在未响应期间预测的情感呈现双峰分布。我们的结果明确揭示了情感与接收性之间的关系。这种关系可能影响移动健康研究的有效性，特别是那些使用学习算法触发生态瞬时评估的研究。因此，作为未来工作，我们提出一种智能触发器，在不影响采样时间点情感的前提下提升生态瞬时评估和即时自适应干预的接收性。