  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.
