Archive (2016–2006)

Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Issue: 2017 (Vol. 56): Issue 6 2017
Pages: 452-460
Ahead of Print: 2017-10-12

Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

Analyzing N-of-1 mHealth Data

Original Article

Y. Cheung (1), P. S: Hsueh (2), M. Qian (1), S. Yoon (3), L. Meli (4), K. M. Diaz (4), J. Schwartz (4, 5), I. M. Kronish (4), K. W. Davidson (4)

(1) Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA; (2) IBM Watson Research Center, Yorktown Heights, NY, USA; (3) School of Nursing, Columbia University, New York, NY, USA; (4) Center for Behavioral Cardiovascular Health, Department of Medicine, Columbia University Medical Center, New York, NY, USA; (5) Department of Psychiatry, Stony Brook University, Stony Brook, NY, USA


Machine Learning, self-quantification, Ecological momentary assessment, exercise behavior, stress-behavior pathway, personal informatics


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 stress-behavior 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.

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