  Recently the use of mobile technologies in Ecological Momentary Assessments
(EMA) and Interventions (EMI) has made it easier to collect data suitable for
intra-individual variability studies in the medical field. Nevertheless,
especially when self-reports are used during the data collection process, there
are difficulties in balancing data quality and the burden placed on the
subjects. In this paper, we address this problem for a specific EMA setting
which aims to submit a demanding task to subjects at high/low values of a
self-reported variable. We adopt a dynamic approach inspired by control chart
methods and design optimization techniques to obtain an EMA triggering
mechanism for data collection which takes into account both the individual
variability of the self-reported variable and of the adherence rate. We test
the algorithm in both a simulation setting and with real, large-scale data from
a tinnitus longitudinal study. A Wilcoxon-Mann-Whitney Rank Sum Test shows that
the algorithm tends to have both a higher F1 score and utility than a random
schedule and a rule-based algorithm with static thresholds, which are the
current state-of-the-art approaches. In conclusion, the algorithm is proven
effective in balancing data quality and the burden placed on the participants,
especially, as the analysis performed suggest, in studies where data collection
is impacted by adherence.
