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.

中文翻译：

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基于计算关键因素的健康行为指数进行行为评估。

目的

过去十年来，无处不在的计算通过各种健康和医疗保健自量化应用程序支持个性化医疗保健。这些应用程序提供了日常生活活动的见解，但无法描述个人习惯对人类健康的全面影响。因此，为了方便个人，我们以适当比例关联了生活习惯，以确定受影响行为对人类福祉的总体影响。

材料和方法

为了研究个人行为的综合影响，我们提出了一种方法，可得出由两个主要过程组成的综合健康行为指数（HBI）：( 1）行为的体重年龄识别（BWI）和（2）健康行为量化和索引（HBQI）建模。BWI过程通过基于预期寿命的体重年龄来确定高等级的贡献行为，而HBQI通过使用健康准则对行为进行量化和索引来得出数学模型。

结果

通过文本挖掘技术确定贡献行为，并由7位Kappa协议等级为0.379的专家进行验证。通过用户体验问卷（UEQ）方法，以用户为中心的真实统计评估应用于HBI服务的影响评估。此HBI服务是为Mining Minds（一种健康管理应用程序）开发的。这项研究涉及韩国健康管理组织的103名注册参与者（对慢性病充满好奇）。他们使用HBI服务超过12周，并通过UEQ和用户反馈对结果进行了评估。使用HBI服务可实现Cronbachα系数大于0.7的服务可靠性，而0.86的刺激系数显示出显着效果。我们观察到值0的整体新颖性。

结论

全面的HBI展示了有关刺激适应健康行为的积极用户体验。HBI服务是独立设计的，可以作为服务工作，因此任何其他启用了健康管理服务的平台都可以使用它来评估人的健康行为指数，以进行推荐生成，行为指示和行为适应。