Research & Summaries Question

Seltzer et al. BMC Medical Informatics and Decision Making

(2019) 19:157

RESEARCH ARTICLE

Open Access

Patients’ willingness to share digital health
and non-health data for research: a crosssectional study
Emily Seltzer1,4, Jesse Goldshear1,4, Sharath Chandra Guntuku1,2,3* , Dave Grande5, David A. Asch1,4,6,
Elissa V. Klinger1,4 and Raina M. Merchant1,2,4

Abstract
Background: Patients generate large amounts of digital data through devices, social media applications, and other
online activities. Little is known about patients’ perception of the data they generate online and its relatedness to
health, their willingness to share data for research, and their preferences regarding data use.
Methods: Patients at an academic urban emergency department were asked if they would donate any of 19
different types of data to health researchers and were asked about their views on data types’ health relatedness.
Factor analysis was used to identify the structure in patients’ perceptions of willingness to share different digital
data, and their health relatedness.
Results: Of 595 patients approached 206 agreed to participate, of whom 104 agreed to share at least one types of
digital data immediately, and 78% agreed to donate at least one data type after death. EMR, wearable, and Google
search histories (80%) had the highest percentage of reported health relatedness. 72% participants wanted to know
the results of any analysis of their shared data, and half wanted their healthcare provider to know.
Conclusion: Patients in this study were willing to share a considerable amount of personal digital data with health
researchers. They also recognize that digital data from many sources reveal information about their health. This
study opens up a discussion around reconsidering US privacy protections for health information to reflect current
opinions and to include their relatedness to health.
Keywords: Data privacy, Data donation, mHealth, Digital health, Social media

Background
In 2012, the retailer Target sent advertisements for baby
products to a teen who had not disclosed her pregnancy
to her parents. Target had concluded the teen was pregnant after she purchased items like unscented lotion and
cotton balls, which figured into algorithms predicting
pregnancy [1]. The algorithm was allegedly accurate, but
the tracking practices of Target were criticized after it
was reported to the public [1, 2]. Individual customers
reportedly complained and reported that predicting
pregnancy from purchases was “creepy” [1]. After the
* Correspondence: [email protected]
1
Penn Medicine Center for Digital Health, University of Pennsylvania, 3400
Civic Blvd, Philadelphia, PA, USA
2
Department of Emergency Medicine, Perelman School of Medicine,
University of Pennsylvania, Philadelphia, PA, USA
Full list of author information is available at the end of the article

public response Target was reported to have modified its
marketing practices, instead of only sending baby supply
coupons to women that their algorithm deemed pregnant, Target would send baby supply coupons with other
home goods items mixed in [1]. “As long as a pregnant
woman thinks she hasn’t been spied on, she’ll use the
coupons” [2]. The sensitive nature of early pregnancy
makes the practice of targeted marketing seem particularly invasive. There are many regulations enacted to
protect traditional clinical health information, but there
is less guidance for how health related digital data
should be protected.
Contemporary practices to safeguard the privacy of
health related data, such as HIPAA privacy rules, emerged
at a time when health data were largely seen as the products of clinical encounters [3]. But health is revealed in a

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License ( which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
( applies to the data made available in this article, unless otherwise stated.

Seltzer et al. BMC Medical Informatics and Decision Making

(2019) 19:157

wide range of individual behaviors that occur outside the
health care system—in purchases, communications,
searches, locations—and an increasing share of those
activities are captured electronically where they can be
linked and analyzed. These data offer promise to advance
research on individual or public health – for instance in
uncovering insights on manifestations and sequelae of
mental health, hospital encounters, and outbreaks [4].
Public acceptability of using these data for health purposes
is, however, unknown and likely dynamic [5]. The promise
of applying disparate digital data to health insights sits
alongside enormous practical uncertainties about logistics,
acceptability, perceived and actual value.
Prior work suggests that many individuals are willing
to share substantial personal information but do not like
to be surprised by how their data are used [5]. The contextual integrity theory includes the idea that perceptions of privacy are based on ethical concerns that
evolve over time [6]. The use of proprietary algorithms
to categorize individuals on the basis of behaviors or
tendencies can be viewed as ‘creepy.’ In the context of
health care, prior work has found that 85% of patients
who reported using social media and who were willing
to participate in research also agreed to share these data
sources and have them linked to their electronic health
record for health research [7]. That consent was provided in the context of active patient care, where trust,
and also perhaps perceptions of information safeguards,
are typically high. Beyond social media however, little is
known about what other digital traces patients would
willingly share with health researchers, under what
circumstances, and for what reason [8].
We used a deception design to credibly evaluate
participants’ willingness to share data, the health relatedness of those digital data sources, and preferences associated with data sharing (e.g. desired information to
receive in return and the individuals with whom participants were willing to share).

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tablet, had 5 core components: willingness to donate
data for health research (now and at death), health
relatedness of digital data, prior experiences with data
privacy, data sharing preferences and concerns, and
demographic information.
We asked participants about 19 different types of
digital data: Facebook, Twitter, Snapchat, Instagram,
EMR, genetic data, prescription history, fitness trackers,
credit card purchases, tax records, online purchase
history, Google searches, music streaming, Yelp reviews,
rideshare history, GPS data, email and text message data.
These data types were chosen based on a larger project
that the Center for Digital Health is conducting.
In an IRB-approved deception design, participants
were asked if they would consider donating any of the
19 different data types to health researchers, and were
told that if they selected “Yes” that they would be
directed to do so immediately, to simulate an actual real
time response. Upon finishing this question block,
participants were informed that they would not actually
be donating their data, and were directed to subsequent
survey questions. Participants used a 5-point scale to
report how strongly they felt that various types of digital
data contained health-related information.
Participants were asked what data they might choose
to donate to researchers, what concerns they would have
about data donation (e.g. fraud, abuse, misidentification),
and who (e.g. friends, family, physician) they would want
to have access to their information [9].
Analysis

Descriptive statistics were used to characterize each of
the components of the survey. Exploratory factor analysis (EFA) was conducted to identify clusters of different
data sources grouped according to participants’ sense of
health-relatedness and willingness to share. EFA was
conducted in R 3.5.1 using Parallel analysis [10, 11] comparing the scree of factors of the observed data with that
of a random data matrix of the same size.

Methods
Aim, design and setting

Patients seeking care in a high volume, urban, academic
Emergency Department from July to November 2017
were approached by research assistants for study participation. Excluded were patients 1) 55

37 (18%)

Race
Black

129 (63%)

White

38 (19%)

Hispanic/Latin(o/a)

10 (5%)

Asian/Pacific Islander

2 (1%)

Multiracial

9 (4%)

Other

17 (8%)

Gender
Female

131 (64%)

Male

71 (35%)

Other

1 (2%)

Education
High School Graduate/GED

113 (55%)

Income
$60,000

30 (15%)

No Answer

33 (16%)

share (Table 2). Based on the dominant content of these
data, we interpreted these groupings as Health/location,
Social Media, Other activities, Politics, Communication,
Financial. Additional file 2: Figure S1 (in the supplement) shows the percentage of patients who reported
using the indicated devices or accessing the type of data
listed.
Health relatedness of digital data

Figure 2 reports participants’ assessments of the health
relatedness of different data sources. Of note, Google
search histories, data from wearables, and email were
considered more health related than genetic data.
Factor analysis revealed 5 discrete themes grouping
different types of data according to perceived health
relatedness (Table 3). Based on the dominant content of
these data, we interpreted these groupings as Social
Media, Health, Financial/location, Apps, Communication/commerce.

Patients were most interested in receiving feedback
about potential risk factors 139 (67%) gleaned from their
data; 155 patients (72%) wanted the information shared
with themselves and 111 (51%) with a health care provider. Only 8 individuals (4%) said they would share
health insights with their social network (Table 4).
Patients also expressed concerns about potential data
and privacy breaches; a majority were concerned that
friends online might inappropriately disclose private information to others 115 (56%), that they might be
defrauded online or their personal information would be
abused 149 (73%), that companies might share information with third parties without consent 153 (74%), and
that companies and websites might use their information
in ways not stated in the privacy policies or user agreements 149 (72%).

Discussion
This study has three main findings. First, patients in this
study were willing to share several non-traditional forms
of data with health researchers now and even more so
after they have died. Second, a non-trivial percentage of
patients recognized that digital footprints left in nonhealth areas such as finance or commerce may reveal information about their health. Third, these patients have
preferences about what health related insights they
would want to learn from their digital data and with
whom they would want to share this information, and
potential pitfalls of digital data sharing.
Participants were willing to share many types of digital
data with researchers, some revealing a willingness to
share presumably sensitive data like tax records and
credit card purchases. These financial data sources may
be highly predictive of health and health outcomes [12].
There are many steps however between sharing and actionable information [13, 14]. Each data source provides
different signal, and the extent of the potential signal is
likely mediated by the amount of data shared, and how
individualized that data are. A growing literature
addresses correlations between digital data and health
outcomes and health care utilization [15–22]. Much of
this research relies on participants sharing personal data
with researchers. Less is known however about patients’
perceptions about how connected these data are with
their health.
The connection between many of these data sources
to health is often obvious and many of those health connections were frequently recognized by study participants. And yet, regulations protecting the privacy of
health information are defined not by health-relatedness,
but by information source [23]. Health-related information arising in the context of clinical care is highly protected under the Health Insurance Portability and

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Page 4 of 8

Fig. 1 Percentage of Patients Agreeing to Donate Data Now & After Death. This figure indicates the proportion of patients who agreed to donate
each data type when approached in the Emergency Department, and the proportion that indicated that they would be willing to donate each
data type after their death

Table 2 Factor analysis ‘willingness to share’
Data type

Factor loading 1
“Health/Location”

Prescriptions

0.708

EHR

0.837

Geolocation

0.641

Genetic data

0.514

Factor loading 2
“Social Media”

Facebook

0.339

Twitter

0.751

Instagram

0.837

Snapchat

0.785

Factor loading 3
“Other activities”

Online purchases

0.345

Music streaming

0.471

Yelp

0.659

Ridesharing

0.763

Fitness tracker

0.732

Voting history

Factor loading 4
“Politics”

Factor loading 5
“Communication”

Factor loading 6
“Financial”

0.884

Email

0.654

Text message

0.735

Google search

0.726

Taxes

0.792

Credit card

0.675

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Fig. 2 Patient Views on the Health Relatedness of Data Types. This figure represents the proportion of participants who answered either “neutral”
or “agree” to whether each of the indicated data types is related to health

Table 3 Factor analysis ‘relatedness to health’
Data type

Factor loading 1
“Social Media”

Facebook

0.634

Twitter

0.988

Instagram

0.870

Snapchat

0.896

Yelp

0.308

Factor loading 2
“Heath”

Google search

0.393

Genetic data

0.573

EHR

0.672

Fitness tracker

0.832

Factor loading 3
“Financial/Location”

Taxes

0.613

Credit card

0.821

Voting history

0.527

Geolocation

0.629

Factor loading 4
“Apps”

Music streaming

0.844

Ridesharing

0.463

Factor loading 5
“Communication
Commerce”

Email

0.798

Text message

0.713

Online purchases

0.339

Seltzer et al. BMC Medical Informatics and Decision Making

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Table 4 Preferences for data sharing and data concerns
Data Preferences

n (%)

Feedback Preferences:
If you donated your electronic data to health researchers, what type of
feedback would you like to receive?

Page 6 of 8

Table 4 Preferences for data sharing and data concerns
(Continued)
Data Preferences

n (%)

Digital Data Concerns (Yes vs. No):
Information I share with friends online may be inappropriately disclosed
by them to others.

Exercise and eating habits

130 (60%)

Habits effect on health

137 (63%)

Yes

115 (56%)

Language analysis from social media

47 (22%)

No

66 (32%)

Comparison to other donors’ data

62 (29%)

Unsure

25 (12%)

Potential risk factors

140 (65%)

Other

30 (14%)

Result Sharing Preferences:
If you donated your electronic data to researchers, who would you want
insights from your data to be shared with?

People who you only know from online are not who they say they are.
Yes

109 (53%)

No

54 (26%)

Unsure

43 (21%)

Other internet users might try to defraud you or abuse your personal
information.

Myself

155 (72%)

Researchers

77 (36%)

Yes

150 (73%)

Health care provider

111 (51%)

No

31 (15%)

Social network

8 (4%)

Unsure

25 (12%)

Family

89 (41%)

Others with similar health conditions

58 (27%)

General Privacy Preferences:
I am generally a private person in my everyday life.
Agree

175 (85%)

Disagree

22 (11%)

Unsure

9 (4%)

I tend to reveal minimal personal information about myself online due
to privacy concerns.
Agree

157 (76%)

Disagree

33 (16%)

Unsure

16 (8%)

I feel uncomfortable when other people have access to my personal
information.
Agree

157 (76%)

Disagree

27 (13%)

Unsure

22 (11%)

I believe that there is no need to be concerned about revealing
personal information online.
Agree

44 (21%)

Disagree

149 (72%)

Unsure

13 (6%)

It does not bother me that a history of my online activities may be
available to 3rd parties online.
Agree

41 (20%)

Disagree

146 (71%)

Unsure

19 (9%)

I regularly use anti-virus/phishing/spamming software, or clear my
browser history/cookies/cache.
Agree

102 (50%)

Disagree

78 (38%)

Unsure

26 (13%)

Online companies and websites might try and share your information to
other parties without explicit consent.
Yes

153 (74%)

No

34 (17%)

Unsure

19 (9%)

Online companies and websites might use your information for
purposes not explicitly stated in the privacy policy.
Yes

149 (72%)

No

34 (17%)

Unsure

23 (11%)

Accountability Act. Health-related information arising in
the context of consumer purchases or social media use
is not. And yet in some cases that latter was perceived
as more health related than the former.
The emergence of direct-to-consumer genetic testing
sites like 23andMe [24–26] can reveal predictive or suggestive information to patients that they may or may not
want to know. For example, when considering feedback
from genetic research, 87% of participants agree that
they would want to have findings shared with them if
researchers found that they had a genetic pattern linked
to a life threatening condition, which was manageable or
curable, 73% if the condition was not life threatening,
and 72% if the condition was life threatening but not
curable [27]. We found similar percentages when we
asked survey respondents if they would want to know if
patterns in their digital data indicate that they had a
higher than average risk for a treatable disease (85%).
When asked if patterns in their data indicated that they
had a higher than average risk for a non-treatable
disease 75% would want to know, and if patterns in their
data indicated that they had a lower than average risk
74% would want to know.

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In April 2018, it was revealed that the firm Cambridge
Analytica had accessed the data of more than 80 million
individuals’ Facebook accounts without their permission.
The public response was considerable, with many concerns raised about data privacy and what companies
know about individuals and the types of information
they share online. This type of large-scale privacy violation has an impact on the trust people have in the security of their digital data, and some people reportedly
deleted their social media accounts after this occurred
[28]. Of note, a large proportion (74%) of patients surveyed in this study had expressed concern that companies might share information with third parties without
consent, a full 6-months before the Cambridge Analytica
activities were reported in the media.
As researchers gain greater insight into the relationship between online activity and an individual’s health,
transparency of these findings is essential to maintain
trust. Increasing focus on returning research findings to
patients is evident in the digital era where there is a
movement toward open science and better patient engagement [29].
A better understanding specifically of health-related
digital footprints is important for being able to provide
guidance to patients about their use of digital platforms
and sharing practices. This emerging field is in its
infancy as many of the most popular social media and
online sites have only been available for slightly more
than ten years.
While providing data back to patients would be a first
step, future work would also focus on the utility of this
data being provided to healthcare providers via an EMR.
Less defined is how this data would be interpreted, or
used, or if it would even be welcomed. Regular reports
of patients’ steps walked, calories consumed, Facebook
status updates, and online footprints might create overwhelming expectations of regular surveillance of questionable value and frustratingly limited opportunities to
intervene even if strong signals of abnormal patterns were
detected [30]. This future work could assess healthcare
providers use of digital data incorporated in an EMR and
focus on issues related to the accuracy, interpretability,
meaning, and actionability of the data [31–35].
This study has limitations. The findings are exploratory and represent a small sample size from a non-representative population. Response rate may have been
influenced by patients being queried in a medical environment and could vary if patients were asked in nonhospital settings. This study also has strengths. Because
we told patients that we would immediately access their
data should they be willing to share it, their willingness
to share more likely represents true preferences, rather
than merely the expressed preferences of a typical hypothetical setting.

Page 7 of 8

Conclusions
Patients use a variety of digital applications that generate
large amounts of data. Our work demonstrates that
participants would be willing to donate some of their
digital data to researchers and clinicians in pursuit of
health-related insights. This work adds to the larger
domain of privacy and health research by connecting
various digital data with perceived health relatedness.
Both the willingness to share data and the perceived
relatedness of those data to health do not follow conventional divisions on which health information privacy
policies are built. Future work should be directed
towards understanding the contexts in which patients
are most likely to donate data for research use, and how
they would want insights shared with them.
Additional files
Additional file 1: Survey Questionnaire. (DOCX 35 kb)
Additional file 2: Figure S1. Patients Reporting Data Usage/Access. This
figure shows the percentage of patients who reported using the
indicated devices or accessing the type of data listed. (DOCX 74 kb)

Abbreviations
EFA: Exploratory factor analysis; EMR: Electronic Medical Records; GPS: Global
Position System; HIPAA: Health Insurance Portability and Accountability Act
of 1996; IRB: Institutional Review Board
Acknowledgements
We thank research assistants Janice Lau, Molly Casey, and Justine Marks for
their assistance with data collection. We also thank the patients for sharing
their responses to the survey.
Authors’ contributions
ES, JG, and RMM originated the study. ES, JG, SCG, DG, DA, and RMM
developed methods, interpreted analysis, and contributed to the writing of
the article. EK, DG, DA, SCG, and RMM assisted with the interpretation of the
findings and contributed to the writing of the article. All authors read and
approved the final manuscript.
Funding
This project was supported by a Robert Wood Johnson Foundation Pioneer
Award (72695). No sponsor of funding source played a role in: “study design
and the collection, analysis, and interpretation of data and the writing of the
article and the decision to submit it for publication.” All researchers are
independent from funders.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not
publicly available due to the IRB guidelines but are available from the
corresponding author on reasonable request.
Ethics approval and consent to participate
This study was approved by the University of Pennsylvania’s Institutional
Review Board (#827652). A written consent was obtained from the
participants.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.

Seltzer et al. BMC Medical Informatics and Decision Making

(2019) 19:157

Author details
1
Penn Medicine Center for Digital Health, University of Pennsylvania, 3400
Civic Blvd, Philadelphia, PA, USA. 2Department of Emergency Medicine,
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,
USA. 3Department of Computer and Information Science, University of
Pennsylvania, Philadelphia, PA, USA. 4Penn Medicine Center for Health Care
Innovation, University of Pennsylvania, Philadelphia, PA, USA. 5Division of
General Internal Medicine, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, USA. 6The Center for Health Equity Research and
Promotion, Michael J Crescenz VA Medical Center, Philadelphia, PA, USA.
Received: 25 February 2019 Accepted: 31 July 2019

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