International audience

Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense–reason–act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods.

Leveraging miniaturized sensor and monitoring technology integrated in easy-to-wear wristband wearables represents a great opportunity for advancing Resilience and Mental Health of e.g. employees that experience high workload. Therefore, it is important to gain insights into the reliability of such technology before far reaching conclusions can be drawn and interventions can be developed. To that aim, we tested three wearable wristband sensor systems (Apple Watch, Microsoft Band and Fitbit Surge) and compared the assessed sensor output with a reliable ground truth. The results showed that heart rate, steps and distance varies considerably around the ground truth during tasks that required body movement. However, during the rest condition (sitting on chair) the heart rate was considered more reliable. It is concluded that caution is warranted while using and interpreting physiological data assessed by the new technology, but, in rest (e.g. pauses, sleep) the wearable' sensors could be used to detect undesirable physiological patterns, indicative of threats to resilience or (mental) health.