On computing critical factors based healthy behavior index for behavior assessment.

Int J Med Inform 2020 Sep 26;141:104181. Epub 2020 May 26.

Department of Computer Science and Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu, South Korea. Electronic address:

Objective: Ubiquitous computing has supported personalized health through a vast variety of wellness and healthcare self-quantification applications over the last decade. These applications provide insights for daily life activities but unable to portray the comprehensive impact of personal habits on human health. Therefore, in order to facilitate the individuals, we have correlated the lifestyle habits in an appropriate proportion to determine the overall impact of influenced behavior on the well-being of humans.

Materials And Methods: To study the combined impact of personal behaviors, we have proposed a methodology to derive the comprehensive Healthy Behavior Index (HBI) consisting of two major processes: (1) Behaviors' Weight-age Identification (BWI), and (2) Healthy Behavior Quantification and Index (HBQI) modeling. The BWI process identifies the high ranked contributing behaviors through life-expectancy based weight-age, whereas HBQI derives a mathematical model based on quantification and indexing of behavior using wellness guidelines.

Results: The contributing behaviors are identified through text mining technique and verified by seven experts with a Kappa agreement level of 0.379. A real-world user-centric statistical evaluation is applied through User Experience Questionnaire (UEQ) method to evaluate the impact of HBI service. This HBI service is developed for the Mining Minds, a wellness management application. This study involves 103 registered participants (curious about the chronic disease) for a Korean wellness management organization. They used the HBI service over 12 weeks, the results for which were evaluated through UEQ and user feedback. The service reliability for the Cronbach's alpha coefficient greater than 0.7 was achieved using HBI service whereas the stimulation coefficient of the value 0.86 revealed significant effect. We observed an overall novelty of the value 0.88 showing the potential interest of participants.

Conclusions: The comprehensive HBI has demonstrated positive user experience concerning the stimulation for adapting the healthy behaviors. The HBI service is designed independently to work as a service, so any other wellness management service-enabled platform can consume it to evaluate the healthy behavior index of the person for recommendation generation, behavior indication, and behavior adaptation.

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http://dx.doi.org/10.1016/j.ijmedinf.2020.104181DOI Listing
September 2020

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