Dyadic interactions of couples are of interest as they provide insight into relationship quality and chronic disease management. Currently, ambulatory assessment of couples' interactions entails collecting data at random or scheduled times which could miss significant couples' interaction/conversation moments. In this work, we developed, deployed and evaluated DyMand, a novel open-source smartwatch and smartphone system for collecting self-report and sensor data from couples based on partners' interaction moments. Our smartwatch-based algorithm uses the Bluetooth signal strength between two smartwatches each worn by one partner, and a voice activity detection machine-learning algorithm to infer that the partners are interacting, and then to trigger data collection. We deployed the DyMand system in a 7-day field study and collected data about social support, emotional well-being, and health behavior from 13 (N=26) Swiss-based heterosexual couples managing diabetes mellitus type 2 of one partner. Our system triggered 99.1% of the expected number of sensor and self-report data when the app was running, and 77.6% of algorithm-triggered recordings contained partners' conversation moments compared to 43.8% for scheduled triggers. The usability evaluation showed that DyMand was easy to use. DyMand can be used by social, clinical, or health psychology researchers to understand the social dynamics of couples in everyday life, and for developing and delivering behavioral interventions for couples who are managing chronic diseases.
翻译:夫妇的DyMand 是一个全新的开放源码智能观察和智能手机系统,用于根据伴侣的互动时刻从夫妇中收集自我报告和感应数据。我们的智能观察算法使用一个伙伴所戴的两个智能观察器之间的蓝牙信号强度,以及一个声音活动检测机器学习算法,以推断伴侣是互动的,然后触发数据收集。我们在为期7天的实地研究中安装了DyMand系统,并收集了13天(N=26)关于社会支持、情感福祉和健康行为的数据,13天(N=26)以来,我们开发、部署和评价了DyMandd,13天(N=26)以来,瑞士异性恋夫妇根据伴侣的互动时刻从夫妇中收集自我报告和感知和感知数据。我们的智能观察算法在应用程序运行时触发了99.1%的预期感知和自我报告数据,77.6%的算盘记录包含合作伙伴的谈话时间,而与43.8%相比,触发数据是慢性病的谈话时间段。我们用Dlabilable 用来为社交和心理学研究人员提供社交行为。