  Wearable sensors such as smartwatches have become ubiquitous in recent years,
allowing the easy and continual measurement of physiological parameters such as
heart rate, physical activity, body temperature, and blood glucose in an
every-day setting. This multi-modal data offers the potential to identify
latent states occurring across physiological measures, which may represent
important bio-behavioural states that could not be observed in any single
measure. Here we present an approach, utilising a hidden semi-Markov model, to
identify such states in data collected using a smartwatch, electrocardiogram,
and blood glucose monitor, over two weeks from a sample of 9 participants. We
found 26 latent ultradian states across the sample, with many occurring at
particular times of day. Here we describe some of these, as well as their
association with subjective mood and time use diaries. These methods provide a
novel avenue for developing insights into the physiology of everyday life.
