Exploring factors impacting sharing health-tracking records
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.
Section snippets
Perspectives on information sharing and privacy
Whereas information sharing has been widely addressed in various studies, sharing health-tracking records is a phenomenon of the recent past. Consequently, there is scarce information about this topic available in the literature. Importantly, HTR contains information that could be classified as sensitive or confidential, requiring special care and handling. To shed light on the sharing of HTR, we investigate it from two perspectives: one pertaining to information sharing, and another pertaining
Research model
The dependent variable in our model is the sharing of health-tracking records, that is, “whom (which category of acquaintances) a respondent shares HTR with”. The main independent variables are health motivation, perceived health status, severity, and age; education is used as a control variable.
Sample
In our research, we analyzed survey data collected by Pew Internet & American Life Project between August 7 and September 6, 2012. The results reported here come from a survey performed by Princeton Survey Research Associates International [62] of 3014 adults living in the United States. Telephone interviews were conducted by landline (1808) and cell phone (1206, including 624 respondents without a landline phone), both geographically disproportionate. A combination of landline and cell random
Method
We employed multinomial logistic regression (MLR) analysis using STATA 13 to investigate the factors influencing sharing HTR. MLR rests on two primary assumptions: (a) lack of multicollinearity, and (b) independence of irrelevant alternatives (IIA) [71].
To check for multicollinearity, we examined the correlations between the independent variables (Table 1). A significant moderate negative correlation was observed between perceived health status (PHS) and severity. However, since VIF analysis
Results
Table 2 presents the multinomial logistic regression results for groups 1 and 2 with response category 3 (i.e. health or medical professionals) set as a reference group (base outcome). Hypothesis 1 predicted that health-motivated individuals would be more likely to share HTR with health or medical professionals vs. other categories of acquaintances. In case of group 1, this hypothesis was supported for all three comparisons (i.e.(1) spouse/partner, (2) relatives, and (4) there acquaintances vs. health or
Discussion
The empirical results of the study shed light on why people of different ages share their health-tracking records with different categories of acquaintances.
First, consistent with the theoretical model by Becker at al. [7] and a number other studies [74], [75], HM has a significantly positive impact on health-related behavior. In particular, health motivation, created, for instance, by changing one’s overall approach to overall health, by asking more questions, or by getting a second opinion,
Conclusions
As is evident from the study the sharing of HTR appears to blend information sharing with the embedded component of information privacy. By adapting the HBM, our study demonstrated that the four factors (HM. PHS, severity, and age) influence the likelihood of health-related actions. As discussed above, the findings with respect to the four categories of acquaintance were consistent with existing theory on HBM. Furthermore, we advanced current knowledge on the issues pertaining to self-tracking
Ethical approval
Not required.
Funding
This research has been funded in part by NSF under Grant no. 0916612.The research has also been supported in part by NSF under Grants no. 1227353, 1419856 awarded to the third author.
Competing interests
None declared.
Acknowledgments
The usual disclaimer applies. We thank the Editorial review board for critical comments that have greatly improved the paper.
References (75)
- et al.
Views on health information sharing and privacy from primary care practices using electronic medical records
Int J Med Inf
(2011) - et al.
Perceived benefits of sharing health data between people with epilepsy on an online platform
Epilepsy Behav
(2012) - et al.
Information sharing concerning schizophrenia in a family member: adult siblings’ perspectives
Arch Psychiatr Nurs
(1993) - et al.
Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention
Gait Posture
(2014) - et al.
Studying users’ computer security behavior: a health belief perspective
Decis Supp Syst
(2009) - et al.
Is breast self-examination predicted by knowledge, attitudes, beliefs, or sociodemographic characteristics?
Am J Prev Med
(1989) - et al.
Is breast self-examination predicted by knowledge, attitudes, beliefs, or sociodemographic characteristics?
Am J Prev Med
(1989) - et al.
Determinants of health counseling practices in hospitals: the patient׳s perspective
Am J Prev Med
(1989) Consumer awareness of name removal procedures: implications for direct marketing
J Direct Mark
(1995)- PRC. Health Fact Sheet: Pew Research Center . Available from:...
On the self-regulation of behavior
The role of situation awareness in naturalistic decision making
The role of feedback in the process of health behavior change
Am J Health Beha
Information sharing online: a research challenge
Int J Knowl Learn
Historical origins of the health belief model
Health Educ Behav
The Health Belief Model and prediction of dietary compliance: a field experiment
J Health Soc Behav
A qualitative study of consumer attitudes to sharing psychosocial information within the multidisciplinary cancer care team
Support Care Cancer
Patient interest in sharing personal health record information: a web-based survey
Ann Intern Med
Patients who share transparent visit notes with others: characteristics, risks, and benefits
J Med Internet Res
Use of patients׳ mobile phones to store and share personal health information: results of a questionnaire survey
Intern Med
Information privacy research: an interdisciplinary review
MIS Q
Internet users’ information privacy concerns (IUIPC): the construct, the scale, and a causal model
Inf Syst Res
Toward a typology of Internet users and online privacy concerns
Inf Soc
The effect of sharing health information on teachers’ production of classroom accommodations
Psychol Sch
Disability, age, and informational privacy attitudes in quality of life technology applications: results from a national web survey
ACM Trans Access Comput
Uncovering patterns of technology use in consumer health informatics
Wiley Interdiscip Rev: Comput Stat
The virtuous circle of the quantified self: a human computational approach to improved health outcomes
Handbook of Human Computation
The quantified self: fundamental disruption in big data science and biological discovery
Big Data
Emerging patient-driven health care models: an examination of health social networks, consumer personalized medicine and quantified self-tracking
Int J Environ Res Public Health
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