Analysis of health consumers' behavior using self-tracker for activity, sleep, and diet

Abstract

Background: With the ever-increasing availability of health information technology (HIT) enabling health consumers to measure, store, and manage their health data (e.g., self-tracking devices), more people are logging and managing their own health data for the purpose of promoting general well-being. To develop and implement effective and efficient strategies for improving personal monitoring devices, a rigorous theoretical framework to explain the health consumer's attitude, intention, and behavior needs to be established. The aim of this study is to verify the HIT acceptance model (HITAM) in the context of the health consumer's attitude, behavioral intention, and behavior of utilizing self-trackers. Furthermore, the study aims to gain better understanding of self-tracking behavior in the context of logging daily activity level, sleep patterns, and dietary habits.

Subjects and methods: Forty-four female college students were selected as voluntary study participants. They used self-trackers for activity, sleep, and diet monitoring for 90 or more consecutive days. The logged data were analyzed and fitted to the HITAM to verify whether the model was suitable for capturing the various behavioral and intention-related characteristics observed.

Results: The overall fitness indices for the HITAM using the field data yielded an acceptable fitness to the model, with all path coefficients being statistically significant. The model accounts for 66.8% of the variance in perceived usefulness, 43.9% of the variance in perceived ease of use, 83.1% of the variance in attitude, and 48.4% of the variance in behavioral intention. The compliance ranking of self-tracking behavior, in order of decreasing compliance, was activity, sleep, and diet. This ranking was consistent with that of ease of use of the personal monitoring device used in the study.

Conclusions: The HITAM was verified for its ability to describe the health consumer's attitude, behavioral intention, and behavior. The analysis indicated that the ease of use of a particular HIT device stands as the most significant barrier in the way of increasing the efficacy of self-tracking.

Keywords: health consumer; health informatics; medical records; quantified-self; self-tracker; telemedicine.

Fig. 1.

Health information technology (HIT) acceptance model and the variables excluded or included in the study.

Fig. 2.

Path diagram of the health information (HIT) technology acceptance model.