Summary
Objectives: The understanding of how stress influences health behavior can provide
insights into developing healthy lifestyle interventions. This understanding is traditionally
attained through observational studies that examine associations at a population level.
This nomothetic approach, however, is fundamentally limited by the fact that the environment-
person milieu that constitutes stress exposure and experience can vary substantially
between individuals, and the modifiable elements of these exposures and experiences
are individual-specific. With recent advances in smartphone and sensing technologies,
it is now possible to conduct idiographic assessment in users’ own environment, leveraging
the full-range observations of actions and experiences that result in differential
response to naturally occurring events. The aim of this paper is to explore the hypothesis
that an ideographic N-of-1 model can better capture an individual’s stress- behavior
pathway (or the lack thereof) and provide useful person-specific predictors of exercise
behavior.
Methods: This paper used the data collected in an observational study in 79 participants
who were followed for up to a 1-year period, wherein their physical activity was continuously
and objectively monitored by actigraphy and their stress experience was recorded via
ecological momentary assessment on a mobile app. In addition, our analyses considered
exogenous and environmental variables retrieved from public archive such as day in
a week, daylight time, temperature and precipitation. Leveraging the multiple data
sources, we developed prediction algorithms for exercise behavior using random forest
and classification tree techniques using a nomothetic approach and an N-of-1 approach.
The two approaches were compared based on classification errors in predicting personalized
exercise behavior.
Results: Eight factors were selected by random forest for the nomothetic decision
model, which was used to predict whether a participant would exercise on a particular
day. The predictors included previous exercise behavior, emotional factors (e.g.,
midday stress), external factors such as weather (e.g., temperature), and self-determination
factors (e.g., expectation of exercise). The nomothetic model yielded an average classification
error of 36%. The ideographic N-of-1 models used on average about two predictors for
each individual, and had an average classification error of 25%, which represented
an improvement of 11 percentage points.
Conclusions: Compared to the traditional one-size-fits-all, nomothetic model that
generalizes population-evidence for individuals, the proposed N-of-1 model can better
capture the individual difference in their stressbehavior pathways. In this paper,
we demonstrate it is feasible to perform personalized exercise behavior prediction,
mainly made possible by mobile health technology and machine learning analytics.
Keywords
Ecological momentary assessment - exercisebehavior - machine learning - stress-behaviorpathway
- personal informatics - self-quantification
