Pattern of active and inactive sequences of diabetes self-monitoring in mobile phone and paper diary users

Nikhil S. Padhye, Jing Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

In a pilot randomized controlled trial involving overweight or obese participants with type 2 diabetes, we find that smartphone users have sharply higher adherence to self-monitoring of diet, physical activity, blood glucose, and body weight, as compared to paper diary users. By characterizing the pattern of adherence with the probability of continuation of active and inactive sequences of self-monitoring, we find that smartphone users have longer active sequences of self-monitoring of all four behaviors that were being monitored. Smartphone users are also quicker to resume self-monitoring of diet and physical activity after a lapse in self-monitoring, whereas paper diary users have shorter inactive sequences for monitoring blood glucose and body weight. The findings are informative for data collection methodology in this burgeoning area of research.

Original languageEnglish (US)
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7630-7633
Number of pages4
ISBN (Electronic)9781424492718
DOIs
StatePublished - Nov 4 2015
Externally publishedYes
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
CountryItaly
CityMilan
Period8/25/158/29/15

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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