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Original Paper
Effectiveness of Digital Forced-Choice Nudges for Voluntary Data
Donation by Health Self-trackers in Germany: Web-Based
Experiment
Katharina Pilgrim, PhD; Sabine Bohnet-Joschko, Prof Dr
Department of Management and Entrepreneurship, Faculty of Management, Economics and Society, Witten Herdecke University, Witten, Germany
Corresponding Author:
Katharina Pilgrim, PhD
Department of Management and Entrepreneurship
Faculty of Management, Economics and Society
Witten Herdecke University
Alfred-Herrhausen-Str 50
Witten, 58455
Germany
Phone: 49 2302926475
Email: katharina.pilgrim@uni-wh.de
Abstract
Background: Health self-tracking is an evidence-based approach to optimize health and well-being for personal self-improvement
through lifestyle changes. At the same time, user-generated health-related data can be of particular value for (health care) research.
As longitudinal data, these data can provide evidence for developing better and new medications, diagnosing rare diseases faster,
or treating chronic diseases.
Objective: This quantitative study aims to investigate the impact of digital forced-choice nudges on the willingness of German
health self-trackers to donate self-tracked health-related data for research. This study contributes to the body of knowledge on
the effectiveness of nonmonetary incentives. Our study enables a gender-specific statement on influencing factors on the voluntary
donation of personal health data and, at the same time, on the effectiveness of digital forced-choice nudges within tracking apps.
Methods: We implemented a digital experiment using a web-based questionnaire by graphical manipulation of the Runtastic
tracking app interface. We asked 5 groups independently to indicate their willingness to donate tracked data for research. We
used a digital forced-choice nudge via a pop-up window, which framed the data donation request with 4 different counter values.
We generated the counter values according to the specific target group needs identified from the research literature.
Results: A sample of 919 was generated, of which, 625 (68%) were women and 294 (32%) were men. By dividing the sample
into male and female participants, we take into account research on gender differences in privacy tendencies on the web and
offline, showing that female participants display higher privacy concerns than male participants. A statistical group comparison
shows that with a small effect size (r=0.21), men are significantly more likely (P=.04) to donate their self-tracked data for research
if the need to take on social responsibility is addressed (the prosocial counter value in this case—contributing to society) compared
with the control group without counter value. Selfish or pseudoprosocial counter values had no significant effect on willingness
to donate health data among male or female health self-trackers in Germany when presented as a forced-choice nudge within a
tracking app.
Conclusions: Although surveys regularly reveal an 80% to 95% willingness to donate data on average in the population, our
results show that only 41% (377/919) of the health self-trackers would donate their self-collected health data to research. Although
selfish motives do not significantly influence willingness to donate, linking data donation to added societal value could significantly
increase the likelihood of donating among male self-trackers by 15.5%. Thus, addressing the need to contribute to society promotes
the willingness to donate data among male health self-trackers. The implementation of forced-choice framing nudges within
tracking apps presented in a pop-up window can add to the accessibility of user-generated health-related data for research.
(J Med Internet Res 2022;24(2):e31363) doi: 10.2196/31363
KEYWORDS
quantified self; health self-tracking; digital nudge; data donation; health data; mobile phone
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Introduction
Self-tracked Health Data for Research
As of April 2020, more than half a million enthusiastic health
self-trackers donated collected data to the federal government
research institute responsible for disease control and prevention
in Germany, via fitness wristbands or smartwatches [1]. As of
February 2021, data records donated have exceeded 170 million
sets, including pulse, blood pressure, weight, temperature,
physical activity, and information on sleep cycles. Regarding
the COVID-19 pandemic, heart rate measurement and physical
activity are of particular interest because an accelerated pulse
while decreasing activity is a likely fever indicator—a typical
COVID-19 symptom. At best, this fever monitor will serve as
an early warning system and predict the spread of coronavirus
in Germany even before case numbers from public health
authorities are available [2].
The approach described is only one instance of how
user-generated health-related data could add value to research
and support public health measures [3]. Moreover, this kind of
real-world data can support the development of pharmaceutical
innovation, accelerate rare disease diagnosis, and improve
chronic disease treatment [4-10]. For the self-tracked data to be
available for research, users have to share, more precisely,
donate their data actively to research institutions [11,12]. In
addition to observing regulatory, ethical, and privacy issues, it
is crucial to know, understand, and evaluate the motives of a
specific target group for data donation [13,14]. Research on
data donation typically uses a population cross-section sample.
Surveys indicate that 4 of 5 Germans are willing to donate their
digital health data anonymously and free of charge for medical
research [15]. As many as 95% of German and US social media
users would specifically donate their data to scientists at
universities and public research institutions [15,16]. The results
can potentially be highly biased, as individuals may
hypothetically signal their willingness to donate data and
perhaps not even engage in health self-tracking at all. Thus, on
the one hand, research lacks information on the willingness to
donate self-tracked health-related data among those who actually
engage in health self-tracking for personal reasons and with
individual goals. On the other hand, we need to determine
whether there are nonmonetary benefits for that target group
that can possibly influence the willingness to donate data
positively.
Research Question and Objectives
We argue that digital forced-choice nudging in general is an
appropriate tool to introduce behavioral change toward donating
personal health-related data among health self-trackers because
health self-trackers are very likely to share their self-tracked
data with third parties and primarily have other intentions for
tracking. Thus, data donation for research might be perceived
as rather unimportant and therefore present an appropriate field
for effective nudging.
This quantitative study aims to investigate whether a digital
forced-choice nudge can influence the willingness of German
health self-trackers to donate self-tracked health-related data
for research. We want to contribute to the body of knowledge
on the effectiveness of nonmonetary incentives derived from
the known needs of the target population. For this, we also
consider research on gender differences in privacy tendencies
on the web and offline. This shows that women display higher
privacy concerns than men. This is in line with both evolutionary
and social role theories. Therefore, we analyzed male and female
health self-trackers separately [17,18]. Our study thus enables
a gender-specific statement on influencing factors on the
voluntary donation of personal health data and, at the same time,
on the effectiveness of digital nudges for health self-trackers in
Germany.
Background and Theory
Nudging
When discussing the active decision to donate self-collected
health data and possible contributing factors, we already know
that various psychological effects influence individuals,
consciously or unconsciously, during their decision-making
process [19,20]. People often act impulsively, emotionally, or
simply out of habit [21]; they are not always able to calculate
the expected consequences of given options and therefore choose
the seemingly best available one [22].
In this context, nudges are tools for influencing behavior in
decision-making processes without resorting to prohibition,
commandments, or economic incentives [19,23,24]. Thus, a
nudge is a nonregulatory approach that attempts to motivate
individual behavior change through subtle alterations in the
choice environments that people face [19,25], whereas a
suggested benefit is embedded in the decision-making process
[26]. Thus, Karlsen and Andersen [27] define nudging as a term
for influencing decisions and behavior using suggestions,
positive reinforcement, and other noncoercive means. Löfgren
and Nordblom [28] show that the likelihood of a nudge having
an effect is higher for choices that the individual perceives as
rather unimportant and at a moment in time with limited
attention. In summary, the behavior of our study group, health
self-trackers in Germany, can potentially be influenced by a
nudge that promises a benefit based on existing target group
needs and is implemented at a point of limited attention.
Nudges in Health Systems
Nudges are widely applied in health systems, with the ultimate
goal of a healthy population. According to Holland et al [29],
most nudging measures aim at a healthier diet, more exercise,
and the reduction of alcohol and tobacco consumption and are
thus frequently applied in preventive interventions. Especially
in the nutrition field, we find a body of evidence for the
effectiveness of nudging interventions: the use of nudges such
as food traffic lights, the prominent placement of healthy food
alternatives, or the transparent display of calories increases the
choice of the healthier option by an average of 16.3% in test
participants [30]. Okeke et al [31] investigated the impact of
haptic (digital detox) nudging (phone vibration) to reduce the
time spent on the web to improve users’well-being, successfully
reducing daily screen time by over 20%. The growing body of
evidence on nudging is also increasingly attracting the attention
of health care insurance providers. They can potentially realize
considerable savings by encouraging their insureds toward
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targeted (healthier) behavior changes while offering the prospect
of bonuses in return.
Digital Nudges
With the extensive use of digital devices, decision-making in
digital choice environments (so-called digital nudging) has
emerged, especially in the field of e-commerce [27,32]. Digital
nudges affect value cocreation by (1) widening resource
accessibility, (2) extending engagement, or (3) augmenting
human actors’ agency [33]. Similar to offline nudges, ethical
considerations for digital nudges are already discussed in the
literature, focusing on topics such as preserving individuals’
freedom of choice or autonomy, transparent disclosure of
nudges, and individual (proself) and societal (prosocial)
goal-oriented justification of nudging [34]. Mirsch et al [32]
pointed out that digital nudging in general is a promising
research area with great potential for improvement and
opportunities, especially regarding user interface, user
experience, and digital service design questions. The future
potential is supported by findings from the study by Hummel
and Maedche [35], who discovered that today, only 62% of
digital nudging treatments are statistically significant. Regarding
the effect size of digital nudges, a quantitative review showed
no difference (median effect size of 21% depending on category
and context) compared with offline settings while offering new
perspectives of individualization [35]. However, research on
smart digital nudges in the health care or data donation field is
limited. Meske et al [36], for example, conclude that digital
nudging in hospitals can positively influence the use of
technology, new value creation, changes in structures, and
consequently financial dimensions of digital transformation,
supporting caregivers as well as caretakers [36]. Regarding
charity program participation, forced-choice nudges are found
to be the most efficient ones [37,38]; a review reveals that
overall default nudges are most effective, and precommitment
strategies are least effective [35].
Hypotheses Development
Overview
To test the effectiveness of digital forced-choice nudges for data
donation among health self-trackers in Germany, we need to
identify the needs of our test group to use the right triggers for
behavior change—in our experiment, donate tracked data. The
prospect of need satisfaction could be a potentially attractive
reward, which might encourage self-trackers to donate their
data in return. To this end, we will derive potentially attractive
counter values (benefits) for framing in a digital forced-choice
nudge based on the known prevailing motives and needs of our
target group and for data donation in general.
Need of Achievement and Power by Self-expertization
People with a diagnosed disease predominantly track vital signs
or biological parameters [39-42]. Intrinsic motivation to improve
disease management by advancing personal disease knowledge
and controlling health indicators (such as glucose or blood
pressure levels) is key for tracking [39-42]. Goals are controlling
symptoms and preventing or delaying disease progression.
People with self-perceived disease risk factors mainly track
their dietary and physical activities. Motives include potential
and subjectively perceived risk prevention (such as obesity) or
the desire to learn and promote a healthier lifestyle [43,44].
People without a diagnosed or self-perceived disease or
prevalence primarily track their nutrition and exercise
parameters. Self-design by performance optimization and
monitoring performance progress improvement is the motivation
behind [45-47]. A less relevant motive is self-entertainment,
which includes natural curiosity, a basic interest, and fun in data
collection and visualization [46,48,49].
Health self-trackers thus collectively possess a desire to use
digital technologies to optimize health and well-being via
self-monitoring [50,51]. Self-motivation, self-discipline, or the
desire for performance enhancement are motives found in every
user group [52,53] and can be labeled as self-expertization [54].
According to McClelland, these motives arise from the need
for achievement and power (over a disease or one’s own body)
[55-59]. As self-expertise is key in every subpopulation with
no regard to personal medical conditions, our first hypothesis
is as follows:
•H1A: The prospect of receiving individualized tips to
improve one’s health has a positive influence on female
health self-trackers’ willingness to donate personal
self-collected health-related data for research.
•H1B: The prospect of receiving individualized tips to
improve one’s health has a positive influence on male health
self-trackers’willingness to donate personal self-collected
health-related data for research.
Need of Self-actualization by Contributing to Society
Research on the motives to donate personal data, for example,
to charity, is consistent with findings on motives supporting
prosocial behavior, such as blood donation [60-63]. Donations
can positively impact self-image and sense of self, as the donor
receives appreciation and care in return [64]. Thus, individual
needs as well as the need for self-actualization are also satisfied
in this context [65]. Kalkman et al [14] pointed out that although
participants recognized the actual or potential benefits of data
donation for research, they expressed concerns about
confidentiality and data abuse [14]. Nonetheless, 2 positive
influences on the willingness to donate personal data exist:
social responsibility or sense of duty is the first and most
influential factor [66]. It refers to indirect reciprocity: giving
back to the community and expecting the same treatment in
return [67,68]. This altruistic motive is predominantly based on
perceived empathy—the willingness to help out of compassion
[69].
Second, an individual’s perception of the significance of their
own contributions to the community is crucial. Nudging
intervention can realize this by emphasizing benefits, such as
potentially accelerating the cure of disease by valid research
findings and improved therapeutic interventions [16,70].
Derived from identified positive influences on data donation in
general and underlying needs, our second hypothesis is as
follows:
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•H2A: The prospect of contributing to the community has
a positive influence on female health self-trackers’
willingness to donate personal self-collected health-related
data for research.
•H2B: The prospect of contributing to the community has a
positive influence on male health self-trackers’willingness
to donate personal self-collected health-related data for
research.
Need of Recognition and Social Belonging
According to Gimpel et al [46], motives for sharing self-tracked
data, such as diet- and exercise-related or vital and biological
parameters, can be referred to as self-association. Cost-benefit
trade-offs regarding sharing tracked data are strongly linked to
situationally perceived experience [71]. On the basis of the
social exchange theory by Homan, users unconsciously or
consciously weigh the costs or disadvantages of disclosure
against personally perceived advantages [72]. Prospects of
satisfying feelings of belonging (to a community) and
identification with personalized and individual data outweigh
possible disadvantages, for example, receiving personalized
advertisements or privacy concerns [46]. Self-association can
be traced back to social and individual needs satisfaction
according to Maslow and Kruntorad [65], as individuals feel
an urge for recognition and belonging and, based on this, possess
a desire for esteem and prestige. We derive hypotheses 3 and 4
according to the identified motives for personal health-related
data sharing as follows:
•H3A: The prospect of recognition by the peer group
(classification of personal tracking results) has a positive
influence on female health self-trackers’ willingness to
donate personal self-collected health-related data for
research.
•H3B: The prospect of recognition by the peer group
(classification of personal tracking results) has a positive
influence on male health self-trackers’willingness to donate
personal self-collected health-related data for research.
•H4A: The prospect of belonging to a peer group (via
personal donation activity) has a positive influence on
female health self-trackers’willingness to donate personal
self-collected health-related data for research.
•H4B: The prospect of belonging to a peer group (via
personal donation activity) has a positive influence on male
health self-trackers’ willingness to donate personal
self-collected health-related data for research.
Methods
Study and Questionnaire Design
To test hypotheses 1 to 4 using digital forced-choice nudges,
we set up a web-based experiment with the questionnaire tool
LimeSurvey for health self-trackers in Germany. We generated
5 different mock-ups of the tracking app, Runtastic. We chose
the app for its continued popularity, familiarity across all age
groups, and duration in the market (since 2009—one of the first
apps for health self-tracking) [73-75]. These mock-ups depicted
the following situation:
the user has just tracked a run of 5.2 km with Runtastic. This
screen in particular is a characteristic of a frequently shared
status update on Facebook or Twitter [76]. It represents the
sharing of personal physical activity by predominantly
recreational athletes via social networks [76]. At this point in
the user journey within the app, users only want to see their
personal stats (and share them).
On completion, a pop-up window with call to action appeared.
The pop-up is a new built-in hurdle before reaching the desired
results. The person is in a state of physical exertion, having just
completed an intense sports session. Attention and interest in
pop-up content could be considered lower at this point [28].
Each pop-up window had a recommendation to the
user—donating the tracked data to research, followed by
information that motivates and helps him choose the suggested
behavior—1 of 4 different nudges (N1 to N4) [27]. Presented
nudges refer to hypotheses H1A to H4B. N1 is a framing nudge
with egoistic benefits, promising individual tips for data
donation based on the need for achievement and power through
self-experimentation. N2 promises a contribution to society and
is thus our prosocial (framing) nudge based on the need for
self-actualization by contributing to society. N3 promises the
comparison of one’s own results with other users (social norm
framing), as the second egoistic nudge, built on the identified
need for recognition. N4 promises joining the data-for-science
community, indicated by a badge within the app, and is thus a
pseudoprosocial nudge that also uses social norm framing
(belonging to a community). The counter value is based on the
identified needs of social belonging and the desire for prestige.
Finally, N0 is the control group, with no offered counter value.
Willingness to donate was queried by assessing the likelihood
of donating on an 11-point scale, ranging from 0% to 100%
(How likely are you to click Donate Now?). Only one of the
pop-up windows was randomly included in each questionnaire.
In addition to three sociodemographic parameters, gender, age,
and education, we added 2 items as inclusion criteria to the
questionnaire. We started by querying devices used for tracking
health-related data (smartphone, smartwatch, fitness tracker,
or none), allowing multiple answers as well as the frequency of
accessing the tracked data (daily, weekly, monthly, fever, or
never).
Recruitment
The recruitment strategy included digital social media channels,
such as Facebook, Instagram, LinkedIn, Xing, and Twitter.
Facebook groups dedicated to fitness and nutrition topics as
well as Instagram stories of female fitness microinfluencers
represented key channels. To question active health self-trackers
in Germany that engage with their tracked data, the defined
exclusion criteria were (1) if participants never used a tracking
device and (2) if participants never actively or consciously
tracked any health-related data.
Data Processing and Statistical Analysis
After collection, the data preparation included cleaning and
organizing the raw data set in Excel (Microsoft Inc). We
diligently checked for errors to eliminate incomplete
questionnaires. Data processing involved encoding text format
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data into numeric indicator variables. Ultimately, our sample
included exclusively ordinal-scaled variables suitable for
statistical analysis in SPSS. We divided the sample into female
and male participants to examine gender-specific differences.
To perform appropriate statistical analysis for the experimental
evaluation, we checked the data distribution. In addition, we
checked for variance homogeneity within the 5 different test
groups for each gender. To validate experimental hypotheses
H1 to H4, we compared our 5 groups with each other for men
and women separately. Regarding the data set characteristics,
we applied the Kruskal-Wallis and Mann-Whitney Utests as
nonparametric tests to compare independent samples with
homogeneous variances. The Kruskal-Wallis test can evaluate
whether there is an actual effect of group affiliation in the first
step. With a subsequent post hoc test, we checked which of the
groups differed significantly. We used the Dunn-Bonferroni
test.
Results
Sample
We collected 1091 questionnaires in January and February 2021.
Following our defined exclusion criteria, we excluded 5.04%
(55/1091) of observations because of participants not using a
tracking device and 0.73% (8/1091) of observations because
they did not track any health-related data actively or consciously.
Furthermore, we removed 9.99% (109/1091) of incomplete
questionnaires. The final sample consisted of 919 participants.
Our data set was not normally distributed but left-sided
(skewness=0.087) and compressed (kurtosis=−1.431; Table 1).
The sample included 68% (625/919) women and 32% (294/919)
men. Overall, 45.1% (414/919) of the participants were aged
between 18 and 34 years, 46% (423/919) of the participants
were aged between 35 and 54 years, and 8.9% (82/919) of the
participants were aged >55 years. Overall, only 0.2% (2/919)
of participants did not finish high school. Overall, 4.5% (41/919)
of the participants were still in school or high school graduates
with no additional formal education. In addition, 43.5%
(400/919) of the participants were attending or had graduated
from college, and 51.8% (476/919) of the participants were
going to or had graduated from a university. In terms of tracking
frequency, 85.4% (785/919) of the participants reported daily
tracking, 10.8% (99/919) of the participants tracked weekly,
1.4% (13/919) of the participants tracked monthly, and 2.4%
(22/919) of the participants tracked less than once per month.
We found that 60.3% (554/919) of our sample used a smartwatch
for health self-tracking. Overall, 43.2% (397/919) of the
participants used a smartphone, and 33% (303/919) of the
participants used a fitness tracker (multiple answers were
possible).
The sample (N=919) was divided into 5 test groups with N0 as
186 (20.2%; control group without nudge) and 4 different nudge
groups (N1=183, 19.9%; N2=163, 17.7%; N3=199, 21.7%; and
N4=188, 20.5%; women and men combined).
Taking gender differences in privacy concerns into account, we
split the sample into female (Table 2) and male (Table 3)
participants for further analysis.
Table 1. Data distribution.
ValuesProbability to donate (men + women)
Participants, n (%)
919 (100)Valid
0 (0)Missing
0.087 (0.081)Value, skewness (SE)
−1.431 (0.161)Value, kurtosis (SE)
Table 2. Description of the female sample—divided into the 5 test groups (n=625).a
Value, mean (SD; SE; range; 95% CI)Participants, n (%)Nudge
41.18 (36.113; 3.205; 0-100; 34.84-47.52)127 (20.3)0
43.91 (31.336; 2.922; 0-100; 38.12-49.70)115 (18.4)1
47.97 (34.901; 3.213; 0-100; 41.60-54.33)118 (18.9)2
45.30 (34.574; 2.832; 0-100; 39.70-50.90)149 (23.8)3
34.83 (36.295; 3.370; 0-100; 28.15-41.50)116 (18.6)4
aTotal: mean 42.77, SD 34.879; SE 1.395; range 0-100; 95% CI 40.03-45.51.
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Table 3. Description of the male sample—divided into the 5 test groups (n=294).a
Value, mean (SD; SE; range; 95% CI)Participants, n (%)Nudge
47.80 (36.863; 4.799; 0-100; 38.19-57.40)59 (20.1)0
48.24 (36.608; 4.439; 0-100; 39.37-57.10)68 (23.1)1
63.33 (33.439; 4.985; 0-100; 53.29-73.38)45 (15.3)2
56.20 (34.279; 4.848; 0-100; 46.46-65.94)50 (17)3
41.25 (35.759; 4.214; 0-100; 32.85-49.65)72 (26.3)4
aTotal: mean 50.10, SD 36.112; SE 2.106; range 0-100; 95% CI 45.96-54.25.
Outcomes of Web-Based Experiment: Nudging Female
Health Self-trackers
The female sample contained 625 questionnaires. Regardless
of the nudge applied, 77% (481/625) of women surveyed were
willing to donate their tracked data for research (with a
probability between 10% and 100%).
The Levene test indicates homogeneous variances for female
health self-trackers (Table 4).
Using the Kruskal-Wallis test, we examined whether the
probability of data donation is the same across the 5 sample
groups. As a result, we had to reject the nil hypothesis because
significant differences (P=.03) exist across at least 2 groups.
As a post hoc test, we applied the Dunn–Bonferroni test for
pairwise group comparison to identify the groups with
significant differences. Ultimately, we found no significant
difference in the likelihood of donating data between the control
group 0 and the 4 test groups. Accordingly, we had to reject the
hypotheses H1A, H2A, H3A, and H4A for female health
self-trackers, as none of the nudges exerted a significant positive
influence on the willingness to donate data. However, there was
a significant difference between group 2, the social nudge, and
group 4, the prosocial nudge (P=.03; Figure 1; Table 5).
Table 4. Levene test of homogeneity of variances (women; N=625).
Significance (Pvalue)Levene statistic (df)Parameters
Probability to donate (women)
.092.007 (4,620)On the basis of the mean
.261.323 (4,620)On the basis of the median
.261.323 (4,548.352)On the basis of the median and with adjusted df
.121.864 (4,620)On the basis of the trimmed mean
Figure 1. Boxplots for willingness to donate among the 5 test groups (women).
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Table 5. Pairwise comparison of nudges (women; N=625).
Adjusted significanceb
(Pvalue)
Significance (Pvalue)Standard test
statistic
Test statistic (SE)Participants, n (%)
Sample 1-sample 2a
.99.151.43733.037 (22.986)243 (38.9)4-0
.25.032.23552.646 (23.552)231 (36.9)4-1
.09.012.59157.423 (22.161)265 (42.4)4-3
.03.0032.96769.431 (23.401)234 (37.4)4-2
.99.40−.851−19.609 (23.038)242 (38.7)0-1
.99.26−1.128−24.386 (21.615)276 (44.2)0-3
.99.11−1.590−36.394 (22.884)245 (39.2)0-2
.99.83−.215−4.777 (22.215)264 (42.2)1-3
.99.47−.716−16.785 (23.452)233 (37.3)1-2
.99.59.54412.008 (22.055)267 (42.7)3-2
aEach row tests the null hypothesis that the sample 1 and sample 2 distributions are the same. Asymptotic significances (2-sided tests) are displayed.
The significance level is P=.05.
bSignificance values have been adjusted by the Bonferroni correction for multiple tests.
The post hoc test results and plot visualization suggest a
potential significant difference between groups 1 and 4 and
between groups 3 and 2. We further applied a median-based
pairwise group comparison using the Mann–Whitney Utest.
The results indicate significant differences between groups 1
and 4 (N=231; P=.01) and between groups 3 and 4 (N=265;
P=.01).
In summary, for female health self-trackers, no offered counter
value, based on egoistic, social, or prosocial motives or needs,
has a significant positive or negative influence on the willingness
to donate tracked data for research. However, the prospect of
showing one’s donation behavior to other users has a negative
effect on willingness to donate data in direct comparison with
groups that receive a prosocial or a self-serving return for their
data donation.
Outcomes of Web-Based Experiment: Nudging Male
Health Self-trackers
Overall, 79.9% (235/294) of the male respondents would donate
their data (with a probability between 10% and 100%),
regardless of the nudge queried (3% more than female
respondents).
The Levene test indicates homogeneous variances for male
health self-trackers (Table 6).
Testing for differences among groups in terms of donation
probability also revealed a significant difference among male
participants (P=.02) using the Kruskal-Wallis test.
A pairwise group comparison also revealed a significant
difference between groups 2 (social nudge) and 4 (prosocial
nudge; P=.02; Figure 2; Table 7).
The Mann-Whitney Utest revealed significant differences
between groups 1 and 2 (P=.04), between groups 0 and 2
(P=.04), and between groups 3 and 4 (P=.02).
As with female health self-trackers, we had to reject hypotheses
H1B, H3B, and H4B for men as well. Thus, the prospect of a
self-serving benefit or displaying data donation activity, named
pseudoprosocial in our experiment, has no significant positive
or negative influence on willingness to donate.
On the other hand, the prospect of making a prosocial
contribution significantly influences the likelihood of donating
self-tracked (health) data with a small effect size (r=0.21;
Z=−2.087; N=104). Accordingly, we could not reject hypothesis
H2B. Comparing means, nudging male health self-trackers in
their donating decision-making process with a prosocial nudge
would increase the willingness to donate data by 15.5%. At the
same time, for male health self-trackers, we found evidence that
open data donation significantly reduces the probability of
donation compared with a secret donation (nudge 2), as well
as compared with test groups 1 and 3, receiving a personal
benefit. In addition, a group comparison of participants receiving
personal tips and participants merely making a prosocial
contribution reveals again that prosocial reasons are superior
to selfish ones and increase the likelihood of donating data in
direct comparisons.
By dividing the sample into male and female participants, we
considered gender differences because of the known privacy
concern differences between men and women. We also split the
sample into different groups regarding age, education, devices
used for tracking health-related data, and tracking frequency
during analysis. The results show that there are no significant
increases or decreases in the willingness to donate self-tracked
health-related data when the sample is divided into the stated
groups. A correlation analysis did not show any significant
positive or negative correlations with the willingness to donate
any other variable but gender (P=.001).
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Table 6. Levene test of homogeneity of variances (men; N=294).
Significance (Pvalue)Levene statistic (df)Parameters
Probability to donate (men)
.480.873 (4,289)On the basis of the mean
.341.127 (4,289)On the basis of the median
.341.127 (4,275.393)On the basis of the median and with adjusted df
.420.975 (4,289)On the basis of the trimmed mean
Figure 2. Boxplots for willingness to donate among the 5 test groups (men).
Table 7. Pairwise comparison of nudges (men; N=294).
Adjusted significanceb
(Pvalue)
Significance (Pvalue)Standard test
statistic
Test statistic (SE)Participants, n (%)
Sample 1-sample 2a
.99.301.04315.441 (14.806)131 (44.6)4-0
.99.221.21817.372 (14.258)140 (47.6)4-1
.22.022.29435.604 (15.522)122 (41.5)4-3
.02.0023.16150.646 (16.022)117 (39.8)4-2
.99.90−.129−1.932 (15.001)127 (43.2)0-1
.99.21−1.244−20.163 (16.207)109 (37.1)0-3
.35.04−2.110−35.205 (16.687)104 (35.4)0-2
.99.25−1.161−18.231 (15.708)118 (40.1)1-3
.40.04−2.054−33.274 (16.203)113 (38.4)1-2
.99.39.86815.042 (17.325)95 (32.3)3-2
aEach row tests the null hypothesis that the sample 1 and sample 2 distributions are the same. Asymptotic significances (2-sided tests) are displayed.
The significance level is P=.05.
bSignificance values have been adjusted by the Bonferroni correction for multiple tests.
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Discussion
Principal Findings and Comparison With Previous
Work
As apps represent digital environments in which users have to
make decisions on an ongoing basis, a neutral choice
presentation is impossible from a behavioral economics point
of view. User interface design is crucial and influences users’
app interactions. Developers need to know and understand user
decisions and motives, as well as the inferable effects of design
on user decision-making, to support desired actions. Our results
show that digital nudges addressing the right user needs seem
to be an action-triggering operation. Specifically, a digital
forced-choice prosocial framing nudge, presented in a pop-up
window, can increase the willingness to donate data among
male health self-trackers by 15.5%. The results are in line with
research on the effectiveness of digital nudging in general (20%)
as well as on the effectiveness of offline nudges in health care
(16%) [30,35]. We need to further examine how the nudge itself
(framing), the presentation (pop-up window vs no pop-up
window), and timing of the digital nudge impact nudge
effectiveness. In general, however, developers and researchers
should consider digital nudges addressing the need for social
responsibility when asking for data donation within this specific
user group.
The experimental results confirm the findings of previous
research on the motives to donate personal data for the health
area. Specifically, for health self-trackers, our study confirms
the results of Skatova and Goulding [66] and Mujcic and
Leibbrandt [68], who found social responsibility or duty to be
the strongest predictor of willingness to donate personal data.
Our experiment demonstrated that the prospect of doing
something good for society positively influences willingness
to donate personal health data. Arguably, the act of donating
has a positive impact on self-image and sense of self. Unlike
blood donation, which provides immediate reciprocal value to
the donor in the form of appreciation and caring (during
donation; Schiefer [64]) by those present, our results suggest
the opposite for health data donation. The public donation of
data and its visibility to third parties, especially peers, can
potentially discourage health self-trackers from making this
donation—they prefer to do so confidentially. Belonging to an
ever more similar group within the group of self-trackers, in
this case, people who donate their health data have no positive
influence on willingness to donate data and more of a negative
influence. Showing charity, therefore, negatively influences
willingness to donate data. This finding should be taken into
account when framing a data donation plea, for example, by
explicitly referring to donor anonymity.
Compared with polls regarding willingness to donate data, our
results show a significantly lower willingness among active
health self-trackers. In contrast to population surveys, which
put a willingness to donate data at 80% or social media users’
willingness at 95% [15,16], only 41% (377/919) of surveyed
health self-trackers would be willing to donate their tracked
data to research with a probability above 50% and only 10%
(92/919) with 100% probability. This discrepancy suggests that
individuals who actively collect health-related data for a
particular purpose value it more. It is important to keep this in
mind when designing approaches to access health-related,
self-tracked data. Research surveying a population cross-section
disregards the hypothetical character of questions and answers,
and thus, results can be biased and lead to ineffective measures.
Our results also show that women, who are evolutionarily more
concerned about protecting their privacy, do not respond to any
of the nudges presented adding to the findings by Tifferet [17]
and Farinosi and Taipale [18], who found these gender
differences, especially in social media users. Regarding the
existing gender gap in clinical trials, addressed by Karp and
Reavey [77], research needs to investigate which motives, needs,
and nudges can increase access to women’s health data equally.
Limitations
The findings have a number of limitations. Owing to recruiting
primarily via social media and fitness influencers, the sample
includes a disproportionately large number of younger and
higher educated people as well as mostly female compared with
male health self-trackers (2/3 to 1/3), which can bias our results.
Thus, the sample may not represent all German health
self-trackers. Furthermore, people engaging in social media and
following a call to action from influencers on Instagram and
from peers in fitness Facebook groups (participating in the
experiment in this case) have fewer privacy concerns and are
more likely to share their health-related information with others.
We did not perform ex ante power calculations to determine
the sample size. Future research may therefore repeat the survey
with a larger sample via additional recruitment channels to
assess the reliability of our findings. In addition, the chosen
geographic focus (Germany) might have biased the results
because of cultural differences in terms of relevance and general
attitudes toward privacy [78]. Further studies could consider
international cross-cultural comparisons to verify the validity
of our findings for a global app market.
Participants’ nonreproducible or untruthful answers can also
limit the results. The reasons could be the chosen and not clearly
comprehensible scale levels or phrasing of the individual benefit.
Future experiments could also imbed nudges in a different
environment (a different app) and use an even more realistic
situation with clickable mock-ups. Our hypothetical request for
web-based health data donation may not represent reality. Future
experiments could implement a real data donation tool within
a popular health self-tracking app to verify our results.
Conclusions
The growing trend toward digital health and increasing
acceptance as well as the use of health apps such as fitness
trackers, digital check-ups, and nutrition apps will contribute
to a significant increase in nudging measures. This study could
aid access to health data for research and long-term care
improvement.
Although selfish motives do not significantly influence
willingness to donate, linking data donation to added societal
value could significantly increase the likelihood of donating
among male self-trackers by 15.5%. Thus, addressing the need
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to contribute to society promotes willingness to donate data
among male health self-trackers and should be emphasized when
designing campaigns to donate health data. The implementation
of forced-choice framing nudges within tracking apps presented
in a pop-up window can add to the accessibility of
user-generated health-related data for research.
Acknowledgments
The study was conducted as part of the ATLAS project, which is funded by the Ministry of Economic Affairs, Innovation,
Digitalization, and Energy of North Rhine-Westphalia (funding code: ITG-1-1).
Authors' Contributions
KP conducted the literature research, conducted the quantitative study, and analyzed and discussed the data. SB-J provided content
support for the process and reviewed the results. All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest
None declared.
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Edited by R Kukafka; submitted 21.06.21; peer-reviewed by M Salimi, T Karen, O El-Gayar, J Nafziger; comments to author 22.09.21;
revised version received 17.11.21; accepted 17.12.21; published 21.02.22
Please cite as:
Pilgrim K, Bohnet-Joschko S
Effectiveness of Digital Forced-Choice Nudges for Voluntary Data Donation by Health Self-trackers in Germany: Web-Based
Experiment
J Med Internet Res 2022;24(2):e31363
URL: https://www.jmir.org/2022/2/e31363
doi: 10.2196/31363
PMID:
©Katharina Pilgrim, Sabine Bohnet-Joschko. Originally published in the Journal of Medical Internet Research
(https://www.jmir.org), 21.02.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution
License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete
bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license
information must be included.
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