There is a current trend of wearable sensing with regards to health. Wearable sensors and devices allow us to monitor various aspects of our lives. Through this monitoring, wearable systems can utilise data to positively influence an individual's overall health and wellbeing. We envisage a future where technology can effectively help us to become fitter and healthier, but the current state of wearables and future directions are unclear. In this paper, we present an overview of current methods used within wearable applications to monitor and support positive health and wellbeing within an individual. We then highlight issues and challenges outlined by previous studies and describe the future focuses of work. General Terms Human Factors

Modern sensing technology is becoming increasingly ubiquitous. Mobile phone sensing data has been used in research to address health and wellbeing; but in the last years, wearable technology became broadly available and popular. This opens new opportunity for health and wellbeing research in the wild. We will present an easy-to-use application to log current emotional states on a widely used smartwatch and collect additional, body sensing data to build a basis for new algorithms, interventions and technologysupported therapy around this data to promote emotional and mental well-being.

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Background and Objectives: Prior research has successfully identified linguistic and behavioral patterns associated with schizophrenia spectrum disorders (SSD) from user generated social media activity. Few studies, however, have explored the potential for image analysis to inform psychiatric care for individuals with SSD. Given the popularity of image-based platforms, such as Instagram, investigating user generated image data could further strengthen associations between social media activity and behavioral health.Methods: We collected 11,947 Instagram posts across 68 participants (mean age = 23.6; 59% male) with schizophrenia spectrum disorders (SSD; n = 34) and healthy volunteers (HV; n = 34). We extracted image features including color composition, aspect ratio, and number of faces depicted. Additionally, we considered social connections and behavioral features. We explored differences in usage patterns between SSD and HV participants.Results: Individuals with SSD posted images with lower saturation (p = 0.033) and lower colorfulness (p = 0.005) compared to HVs, as well as images showing fewer faces on average (SSD = 1.5, HV = 2.4, p < 0.001). Further, individuals with SSD demonstrated a lower ratio of followers to following compared to HV participants (p = 0.025).Conclusion: Differences in uploaded images and user activity on Instagram were identified in individuals with SSD. These differences highlight potential digital biomarkers of SSD from Instagram data.