  Self-supervision may boost model performance in downstream tasks. However,
there is no principled way of selecting the self-supervised objectives that
yield the most adaptable models. Here, we study this problem on daily
time-series data generated from wearable sensors used to detect onset of
influenza-like illness (ILI). We first show that using self-supervised learning
to predict next-day time-series values allows us to learn rich representations
which can be adapted to perform accurate ILI prediction. Second, we perform an
empirical analysis of three different self-supervised objectives to assess
their adaptability to ILI prediction. Our results show that predicting the next
day's resting heart rate or time-in-bed during sleep provides better
representations for ILI prediction. These findings add to previous work
demonstrating the practical application of self-supervised learning from
activity data to improve health predictions.
