Exploring factors impacting sharing health-tracking records

highlights

• HBM was applied as a theoretical framework to investigate HTR sharing phenomena.

• Two types of HTR are explored: (a) weights, diet, (b) blood pressure, sugar, etc.

• Health-motivation leads to sharing HTR with doctors vs. other cat. of acquaintances.

• Individuals with a higher PHS are less likely to share HTR with doctors.

Abstract

With the introduction of modern health technologies, the phenomenon of patients tracking health records has increased considerably in the recent past. The current study aims to examine the sharing of health-tracking records (HTR) by patients with different categories of acquaintances (spouse/partner, relatives, doctors, other). In this paper, we explore the critical factors that impact sharing of self-tracked information across two levels of sensitivity of HTR. Our study investigates how health motivation (HM), perceived health status (PHS), severity of health, and age influence sharing of self-tracked information. To do so, we adapted and applied the Health Belief Model (HBM) as a theoretical framework. The study employed multinomial logistic regression analysis to investigate the various factors that influence sharing of two types of HTR with different acquaintances. The statistical results were weighted to correct for known demographic discrepancies. Results indicated that HM, severity, and age substantially increase the likelihood of sharing HTR with health or medical professionals in comparison to other categories. In contrast, PHS increases the likelihood of information sharing with “other” acquaintances as opposed to with doctors. These findings are consistent with existing theory on HBM, wherein behavior appears to be predicted on the basis of/by both the individual׳s motivation to achieve an outcome and the expectation that a specific action will result in that outcome.

Introduction

As one of the greatest Roman poets Publius Vergilius Maro (Virgil) wrote, “The greatest wealth is health.” Indeed, almost everyone cares about his or her own health. Modern health technologies, such as computer programs, websites and online tools, applications for mobile devices, and medical sensors, are motivating many of today’s consumers and patients to become even more active participants in their own care. According to a Pew Internet Health Study [1], 7 in 10 U.S. adults track at least one health indicator for themselves (e.g. weight, diet, exercise routine, blood sugar, sleep patterns, headaches, etc.). Of those adults, 34% share their health-tracking records (HTR) or notes with others, such as relatives or healthcare professionals. Yet, others prefer not to disclose sensitive health data, thus maintaining their privacy. Self-tracking of health indicators can be used for self-reflection to help people become more aware of their own behavior [2], make better decisions [3], and even affect health care behavior [4].

Keeping self-tracked health information often leads to concerns about exposure of these data (similar to concerns about personal health information in electronic medical record systems) and therefore, raises privacy concerns, especially if the information is recorded and stored electronically, locally, or in “the cloud.” Such concerns are also manifest when self-tracked information is stored offline. Risks stemming from exposure of such information include, but are not limited to, identity theft, personal image damage, and reputation damage. Even though health information tracking and sharing of records afford numerous benefits to consumers and patients, they could also increase the likelihood of unauthorized privacy disclosure, thereby producing negative consequences.

Sharing of HTR appears to blend the information-sharing phenomenon with the embedded component of information privacy. The sharing can occur either offline or online—not only can one verbally share sensitive information on self-tracked health indicators with acquaintances in personal communication, but also share such information using Internet tools, such as social media, health social networks, health-tracking services, blogs, etc., thus raising considerable concerns about privacy. Adapting the definition of Rafaeli and Raban [5], we define information sharing as the act of providing or contributing information (e.g. HTR) with the aim of receiving an answer or reply, in the form of words or through some action. A sizable body of literature deals with information sharing issues, but the issues of patient sharing of health tracking records remain largely unaddressed. This study focuses on the issues pertaining to sharing HTR of two different types with four categories of acquaintances. Particularly, it explores the critical factors that influence sharing of HTR by investigating how health motivation, perceived health status, severity, and age impact the sharing of one’s health indicators with spouse/partner, relatives, physician or healthcare professional, and other acquaintances. We use the Health Belief Model [6] as the theoretical basis for investigating the factors that impact sharing HTR with others—specifically, by adapting a model proposed by Becker et al. [7]. Additionally, the study explores whether sharing HTR behavior might differ between individuals of different ages. The findings could be of value for scholars studying privacy in healthcare, engineers and software developers working in the field of self-tracking IT-artifacts, or policy makers addressing issues of online privacy. The research question can be summarized as follows:

RQ: What is the impact of health motivation, perceived health status, severity, and age on the likelihood of a patient sharing different types of HTR with their spouse/partner, relative, doctor, or other acquaintances?

The paper is organized as follows. The next section reviews the literature on information sharing, information privacy, and self-tracking. The paper subsequently discusses the research model, which is followed by an explication of the method used. After presentation of the results, the paper concludes with a discussion of those findings and their implications.

This research paper makes two significant contributions to the literature. First, it explores how health motivation impacts sharing of health-tracking records with different categories of acquaintances. Second, it adapts the Health Belief Model while applying that model to the context of sharing self-tracked health information. Additionally, the paper advances current knowledge on the issues pertaining to the concept of self-tracking by examining them from the perspectives of information sharing and information privacy.