  The attention for personalized mental health care is thriving. Research data
specific to the individual, such as time series sensor data or data from
intensive longitudinal studies, is relevant from a research perspective, as
analyses on these data can reveal the heterogeneity among the participants and
provide more precise and individualized results than with group-based methods.
However, using this data for self-management and to help the individual to
improve his or her mental health has proven to be challenging.
  The present work describes a novel approach to automatically generate
personalized advice for the improvement of the well-being of individuals by
using time series data from intensive longitudinal studies: Automated Impulse
Response Analysis (AIRA). AIRA analyzes vector autoregression models of
well-being by generating impulse response functions. These impulse response
functions are used in simulations to determine which variables in the model
have the largest influence on the other variables and thus on the well-being of
the participant. The effects found can be used to support self-management.
  We demonstrate the practical usefulness of AIRA by performing analysis on
longitudinal self-reported data about psychological variables. To evaluate its
effectiveness and efficacy, we ran its algorithms on two data sets ($N=4$ and
$N=5$), and discuss the results. Furthermore, we compare AIRA's output to the
results of a previously published study and show that the results are
comparable. By automating Impulse Response Function Analysis, AIRA fulfills the
need for accurate individualized models of health outcomes at a low resource
cost with the potential for upscaling.
