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Impact factor analysis: combining prediction with parameter ranking to reveal the impact of behavior on health outcome

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Abstract

An increasing number of healthcare systems allow people to monitor behavior and provide feedback on health and wellness. Most applications, however, only offer feedback on behavior in form of visualization and data summaries. This paper presents a different approach—called impact factor analysis—in which machine learning techniques are used to infer the progression of a primary health parameter and then apply parameter ranking to investigate which behavioral data have the highest ‘impact’ on health. We have applied this approach to improve the MONARCA personal health application for patients suffering from bipolar disorder. In the MONARCA system, patients report their daily mood score and by analyzing self-reported and automatically sensed behavioral data with this mood score, the system is able to identify the impact of different behavior on the patient’s mood. We report from a study involving ten bipolar patients, in which we were able to estimate mood values with an average mean absolute error of 0.5. This was used to rank the behavior parameters whose variations indicate changes in the mental state. The rankings acquired from our algorithms correspond to the patients’ rankings, identifying physical activity and sleep as the highest impact parameters. These results revealed the feasibility of identifying behavioral impact factors. This data analysis motivated us to design an impact factor inference engine as part of the MONARCA system. To our knowledge, this is a novel approach in monitoring and control of mental illness, and we argue that the impact factor analysis can be useful in the design of other health and wellness systems.

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Acknowledgments

This work has been done in close collaboration with a group of clinicians and patients from the Copenhagen Affective Disorder Clinic at the University Hospital of Copenhagen. MONARCA is funded as a STREP project under the FP7 European Framework program. More information can be found at http://monarca-project.eu/ and http://pit.itu.dk/monarca.

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Authors and Affiliations

  1. Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, USA

    Afsaneh Doryab

  2. Pervasive Interaction Technology Laboratory (PIT Lab), IT University of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen, Denmark

    Mads Frost & Jakob E. Bardram

  3. Psychiatric Center Copenhagen, Department O, 6233, University Hospital of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark

    Maria Faurholt-Jepsen & Lars V. Kessing

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  1. Afsaneh Doryab
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  2. Mads Frost
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  3. Maria Faurholt-Jepsen
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  4. Lars V. Kessing
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  5. Jakob E. Bardram
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Correspondence to Afsaneh Doryab.

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Doryab, A., Frost, M., Faurholt-Jepsen, M. et al. Impact factor analysis: combining prediction with parameter ranking to reveal the impact of behavior on health outcome. Pers Ubiquit Comput 19, 355–365 (2015). https://doi.org/10.1007/s00779-014-0826-8

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  • DOI: https://doi.org/10.1007/s00779-014-0826-8

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