Effect of Values and Technology Use on Exercise:
Implications for Personalized Behavior Change Interventions
Yelena Mejova and Kyriaki Kalimeri
ISI Foundation, Turin, Italy
Technology has recently been recruited in the war against the ongo-
ing obesity crisis; however, the adoption of Health & Fitness appli-
cations for regular exercise is a struggle. In this study, we present a
unique demographically representative dataset of 15k US residents
that combines technology use logs with surveys on moral views,
human values, and emotional contagion. Combining these data, we
provide a holistic view of individuals to model their physical exer-
cise behavior. First, we show which values determine the adoption
of Health & Fitness mobile applications, ﬁnding that users who
prioritize the value of purity and de-emphasize values of conformity,
hedonism, and security are more likely to use such apps. Further, we
achieve a weighted AUROC of .673 in predicting whether individual
exercises, and we also show that the application usage data allows
for substantially better classiﬁcation performance (.608) compared
to using basic demographics (.513) or internet browsing data (.546).
We also ﬁnd a strong link of exercise to respondent socioeconomic
status, as well as the value of happiness. Using these insights, we
propose actionable design guidelines for persuasive technologies
targeting health behavior modiﬁcation.
Health; Exercise; Technology Use; Mobile; Moral Values
ACM Reference Format:
Yelena Mejova and Kyriaki Kalimeri. 2019. Effect of Values and Technology
Use on Exercise: Implications for Personalized Behavior Change Interven-
tions. In 27th Conference on User Modeling, Adaptation and Personalization
(UMAP ’19), June 9–12, 2019, Larnaca, Cyprus. ACM, New York, NY, USA,
10 pages. https://doi.org/10.1145/3320435.3320451
Over the last half-century, the daily occupation-related energy expen-
diture of US workers has decreased by more than 100 calories, with
the proportion of jobs requiring at least moderate intensity physical
activity declining from 48% to 20% in 2008 [
]. Sedentary lifestyle
– one that includes TV watching and gaming and lacks vigorous
exercise – has also been found to be strongly related to the childhood
obesity epidemic .
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Technology has been an essential factor in changing lifestyles.
Over the years, TV has been seen as a replacement of physical activ-
ity, a channel for advertisement of nutrient-poor food, and increasing
prevalence of “mindless” eating [
]. The arrival of mobile tech-
nology has been shown to contribute to the sedentary behaviors [
However, this has not stopped both entrepreneurs and public health
ofﬁcials from attempting to use new technology to encourage behav-
ior change. Unfortunately, within the industry, user retention is an
ongoing struggle, with 62% of mHealth app publishers report digital
solutions with less than 1,000 monthly active users [
]. Indeed, the
latest research shows a complex relationship between psychology,
technology use, and exercise. Mediation analyses ﬁnd that increased
physical activity associated with health app use is related to feelings
of self-efﬁcacy [
], with yet other insights linking exercise to being
extroverted, neurotic, and less agreeable [
], as well as having
implications for mental health [
]. For a better understanding of
the relationship between psychology, technology, and exercise, it
is necessary to model users of new technologies [
] and to design
effective health interventions.
This study is a unique view of the interaction between technol-
ogy use, demographics, and value systems of a representative US
population sample, allowing for rich user modeling in the aims of
promoting exercise. Just over 15k participants ﬁlled in the ques-
tionnaires including the following psychometric measures: Moral
], Schwartz Basic Human Values [
], and Emo-
tional Contagion [
]. Along with these, 5,008 respondents agreed
to allow the capture of their desktop browsing data, whereas another
2,625 allowed to capture their mobile app usage. Using this data, we
contribute the following insights on psychological markers of health
app use and the actual exercise behavior:
•Modeling “Health & Fitness” application use
to psychometric and demographic variables, we ﬁnd a marked
difference in application usage between the two genders, as
well as signiﬁcant negative relationship between the values of
tradition,conformity,hedonism, and security, while positive
•Predicting the engagement in physical exercise
above variables, as well as browsing and application use data,
we show a marked increase in classiﬁcation performance from
baseline demographic model with the addition of psychome-
tric features, as well as application usage data, but with a
smaller contribution of desktop browsing data.
•Revealing determinants of exercise
among the types of vari-
able, conﬁrming a signiﬁcant effect of education and wealth
on healthy behaviors [
], as well as showing signiﬁcant re-
lationships with the view that health is a choice, positive
arXiv:1903.11579v1 [cs.HC] 27 Mar 2019
UMAP ’19, June 9–12, 2019, Larnaca, Cyprus Mejova & Kalimeri
association with happiness emotional contagion and stimula-
tion value, with downloading a Health & Fitness app being
another strong predictor.
•Comparing the exercise behaviors across mobile applica-
, we show those tracking a particular kind of exercise,
such as running or cycling, are associated with more users
reporting exercising regularly, than those for general health
tracking or women’s health tracking.
We conclude with concrete suggestions of employing this knowl-
edge in the design, personalization, and deployment of technologies
for an effective lifestyle change and health outcomes intervention.
2 RELATED WORK
Technology is now commonly used to monitor behavior and physical
]. Digital data from smartphones were initially used
as simple activity monitoring sensors [
]; however, over the last
years, their integration with user-generated content led to more
sophisticated personalized interventions aiming at motivating the
users to increase their physical activity level and encouraging a
healthier lifestyle [
]. Researchers have tried apps with different
messaging strategies [
], personalized exercise recommendation
], as well as utilizing machine learning via supervised learning
] and reinforcement learning [
]. Others help users
ﬁnd exercise partners [
], provide educational materials [
and emotional support [
] (see [
] for a recent survey on
personalized health interventions).
To understand the impact of such interventions, researchers exam-
ined the role of individual characteristics, attitudes, and lifestyle of
], demographic attributes such as gender, age, socioe-
conomic factors, and technology literacy [
]. For instance,
using self-reported technology use, [
] found that two-thirds of
their participants were using a smartphone. This subset was younger,
more likely to have a university degree with higher socioeconomic
status, and was more likely to engage physical activity. A substantial
proportion of their population was not engaged in Health & Fitness
apps; however, those who were were more motivated to change or
maintain a healthy lifestyle. A further association was conﬁrmed
between smartphone use and health literacy [
], and an association
with age, with seniors (65 years and older) using digital health at
much lower (but steadily increasing) rates [
]. Unlike these
previous studies relying on self-reported data based on surveys, our
data present a snapshot of desktop and mobile use which provides
valuable ground truth for tech-related behaviors (as well as a com-
plementary rich demographic baseline).
Despite being an active research direction, the consideration of
the psychological aspects of the individual such as personal views,
values, and emotional states, in tech-driven intervention remains
hugely unexplored [
]. Human values are known to inﬂuence
people’s actions [
] but received little attention in studying their
relationship with attitudes regarding healthy lifestyles. Regarding
human and moral values in sports, Lee et al. [
] examined the
value-expressive function of attitudes and achievement goal theory in
predicting the moral attitudes of young athletes. Ball et al. [
the individual preferences for social support according to the values
system in following a healthy lifestyle. Apart from these studies,
human values were only considered in cases where an individual
deviated from a normative of healthy lifestyle [
] such as depression
] or mental disorders [
]. Among works closest to our intention,
Lathia et al. [
] assessed the relationship between physical activity
and happiness via a smartphone app concluding that people that
exercise more are happier. In this study, we contribute a unique
combination of psychometric measures, spanning morals, values,
and emotional contagion, to better understand technology use and
engagement in physical activity.
3 DATA COLLECTION
Data presented in this study spans 15,021 subjects in the United
States of America, selected using probabilistic, representative sam-
pling methodology, all of whom were incentivised to participate.
After receiving informed consent from all participants for the col-
lection, storage, and analysis of the data, as well as the acceptance
, we administered a series of questionnaires
to gather demographic and psychometric data. Also, we asked the
participants for the access to either their basic mobile or desktop data
for one month, resulting in desktop activity data for 5,008 people
(2,823 women) and mobile activity data for 2,625 people (1,544
women). The latter subset with activity data has been discussed in
. Below we describe the data collected and used in this study2.
The intake survey covered basic demographic factors (age, gender,
ethnicity), geographic factors (home location, expressed at the zip
code level), socioeconomic factors (educational level, marital status,
parenthood, wealth, income), health-related factors (exercise, smoke,
and weight issues) and political orientation. Table 1 presents the
complete list of the demographic information gathered, along with
the respective range of values for all the 15,021 participants.
3.2 Psychometric Measures
Moral Foundations. To measure the values of the participants,
we employ the Moral Foundation Theory [
] which we opera-
tionalized via the Moral Foundations Questionnaire (MFQ) [
validated measure of the degree to which individuals endorse each
of ﬁve dimensions:
care/harm, basic concerns for the suffering of others, includ-
ing virtues of caring and compassion;
fairness/cheating, concerns about unfair treatment, inequality,
and more abstract notions of justice;
loyalty/betrayal, concerns related to obligations of group
membership, such as loyalty, self-sacriﬁce, and vigilance
authority/subversion, concerns related to social order and
the obligations of hierarchical relationships like obedience,
respect, and proper role fulﬁllment;
purity/degradation, concerns about physical and spiritual con-
tagion, including virtues of chastity, wholesomeness, and
control of desires.
For privacy considerations, the data will be made available upon request, exclusively
for the scientiﬁc community.
Effect of Values and Technology Use on Exercise UMAP ’19, June 9–12, 2019, Larnaca, Cyprus
Table 1: Complete list of the demographic attributes collected and their respective ranges for the entire sample of 15,021 participants.
Attribute Demographic Variables Sample size Attribute Demographic Variables Sample size
Range (N=15,021)Range (N=15,021)
Age 18-24 1,636 (10.8%) Political Party Democrat 6,227 (41.4%)
25-34 2,583 (17.1%) Republican 4,455 (29.6%)
35-49 3,770 (25%) Libertarian 429 (2.8%)
50-54 1,642 (10.9%) Independent 3,910 (26%)
55-64 2,707 (18%)
65+ 2,683 (17.8%)
Education College Graduate 4,854 (32.3%) Wealth 50k or less 5,520 (36.7%)
Post Graduate 3,409 (22.6%) 50k-100k 2,087 (13.8%)
Some College 3,810 (25.3%) 100k-250k 2,375 (15.8%)
High-school 1,832 (12.1%) 250k-500k 2,166 (14.4%)
Trade School 949 (6.3%) 500k-1000k 1,627 (10.8%)
1000k or more 1,246 (8.2%)
Ethnicity Asian 669 (4.4%) Weight Issues No 8,709 (57.9%)
African American 1,761 (11.7%) Yes 6,312 (42%)
White 11,042 (73.5%)
Hispanic 1,335 (8.8%)
Exercise No 6,631 (44.1%) Parent No 5,613 (37.4%)
Yes 8,390 (55.8%) Yes 9,408 (62.6%)
Gender Female 8,409 (55.9%) Smoker No 13,150 (87.5%)
Male 6,612 (44.1%) Yes 1,871 (12.4%)
Income 20k or less 1,384 (9.2%) Marital Status Divorced 1,409 (9.3%)
20k-30k 1,389 (9.2%) Single 3,509 (23.3%)
30k-50k 2,785 (18.5%) Married 8,037 (53.5%)
50k-75k 3,246 (21.6%) Living Together 1,444 (9.6%)
75k-100k 2,601 (17.3%)
100k-150k 2,386 (15.8%) High Blood No 12,025 (80%)
150k-200k 745 (4.9%) Pressure Yes 2,996 (20%)
200k or more 485 (3.2%)
The questionnaire is based on self-assessment evaluations and
consists of 30 items, resulting in a unique numerical value from 0-30
per person. According to the MFQ, six items (on a 6-point Likert
scale) per foundation were averaged to produce the individuals’
scores on each of the ﬁve foundations.
Schwartz Basic Human Values. We assess the Schwartz human
values employing the Portrait Values Questionnaire [
validity across cultures is validated in studies performed on 82 coun-
tries and samples belonging to highly diverse geographic, cultural,
linguistic, religious, age, gender, and occupational groups. The ques-
tionnaire is based on self-assessments resulting in a numerical value
per person for each of the ten basic values:
self-direction, independent thought, action-choosing, creating,
stimulation, need for variety and stimulation to maintain an
optimal level of activation;
hedonism, related to organismic needs and the pleasure asso-
ciated with satisfying them;
achievement, personal success through demonstrating compe-
tence according to social standards;
power, the attainment or preservation of a dominant position
within the more general social system;
security, safety, harmony, and stability of society, of relation-
ships, and self;
conformity, restraint of actions, inclinations, and impulses
likely to upset or harm others and violate social expectations
tradition, symbols and practices or groups that represent their
shared experience and fate;
benevolence, concern for the welfare of close others in every-
universalism, this value type includes the former maturity
value type, including understanding, appreciation, tolerance,
and protection for the welfare of all people and nature.
UMAP ’19, June 9–12, 2019, Larnaca, Cyprus Mejova & Kalimeri
The questionnaire is based on self-assessment evaluations on
a 7-point Likert scale. Following [
], we average the respective
items per value, and we account for individual differences. The
above ten values can be clustered into four higher order values,
so-called quadrant values and into two dimensions, as the sum of
the individual items of which they consist: Openness to change
(self-direction, stimulation) vs. Conservation (security, conformity,
tradition) and Self-enhancement (universalism, benevolence) vs. Self-
transcendence (power, achievement). Therefore, the ﬁrst dimension
captures the conﬂict between values that emphasize the indepen-
dence of thought, action, and feelings and readiness for change and
the values that highlight order, self-restriction, preservation of the
past, and resistance to change. The second dimension captures the
conﬂict between values that stress concern for the welfare and inter-
ests of others and values that emphasize the pursuit of one’s interests
and relative success and dominance over others. Hedonism shares
elements of both openness to change and self-enhancement.
Emotional Contagion. Emotional contagion is the phenomenon
that individuals tend to feel emotions, such as happiness, or sadness,
triggered by the feelings expressed by the people with whom they
]. In this study, we employ the well-established emotional
contagion scale (EC) [
]. The 15-item questionnaire is based on
self-assessment evaluations on a 5-point Likert scale. It assesses
mimetic tendency to ﬁve basic emotions (love, happiness, fear, anger,
and sadness), measuring the individual differences in susceptibility
to “catching” and empathizing the emotions of others.
3.3 Digital Data
Desktop Browsing Data. For the participants who permitted the
logging of their desktops’ web browsing data, 5,008 in total, we
capture: (i) the domain names, and (ii) the average time spent online
and (iii) the number of visits per day on each domain. All this
information is aggregated by day, and only the domain names (and
not the page or section of the websites) are stored, to ensure the
privacy of the participants. Users with fewer than
domains are discarded. We then assign to each domain name a
category label according to its content .
Mobile Data. Participants are also asked to download an appli-
web browsing activity and application usage, and 2,625 agreed to be
Application Data. Application usage was captured whenever
the application was running in the “foreground”. Foreground
usage means an application is open on someone’s device, re-
gardless of whether the application is currently being engaged
with or not. Application usage data for each participant in-
cluded records of the date and time stamp, the local time zone,
and time spent on the application (in seconds). Moreover, we
assigned to each application the category label provided by
the Google Play Store3.
Mobile browsing Data. URL data was captured from the
native browser on the subject’s device (not any 3rd party
browsers). URLs for both secure and non-secure trafﬁc were
The assignment was performed parsing the application data from the Google Play
Store using the following project.
captured, though only the URL domain was stored in consid-
eration of privacy. Similar to the desktop browsing data, users
with a number of visits fewer than
are discarded from the analysis leaving us with a total of
2,406 participants.The domains are classiﬁed as above for the
Desktop users .
Noteworthy is the fact that “Mobile” and “Desktop” browsing
data provide the same information, they only express different modes
of web navigation, i.e. mobile vs desktop. See [
] for a detailed
description of the data.
4 HEALTH APP USE
We begin by examining the usage of mobile applications (apps) in
the Health & Fitness category. These apps include those associated
with particular wearables like Fitbit and Garmin Connect, activity
trackers like MapMyRun,RunKeeper,Nike+ Run Club, and weight
management including Lose It! and WW (Weight Watchers). The only
demographic variable associated with getting such an app (more
precisely, opening it at least once in the time of observation), is
gender, with females 45% more likely to get one than males. Though
the gender division is not evenly distributed across applications, with
those marketed for tracking running activity (such as RunKeeper
and MapMyRun) being 24.9% more likely to be adopted by females,
whereas those for walking (Walkroid and MapMyWalk) are 101%
more likely (that is, twice as likely). The distinction is even greater
for weight loss applications, with females 168% more likely to adopt
one than males. Notably, these gender distinctions have not been
revealed in recent surveys [19, 33].
Considering the psychometric attributes, we run a linear model
) to predict the adoption of any Health & Fitness appli-
cation, with the coefﬁcients plotted in Figure 1, whiskers marking
95% conﬁdence intervals, and those signiﬁcant at
in green. We ﬁnd a negative relationship with values associated
with tradition,security,hedonism, and conformity, as well as with
concerns about hypocrisy increasing in the society, and a positive
relationship with purity value. Emotional contagion results show a
positive relationship with sadness but a negative with fear. These
trends point to people less concerned about societal traditions, who
are less inﬂuenced by caution or fear, and those striving towards
physical or spiritual purity.
Finally, we ask which health applications are most associated
with self-reported exercise (deﬁned in more detailed in the following
section). To answer this question, we consider all applications in the
Health & Fitness category having at least ten users in our dataset
and compute the proportion of such users who self-report exercising.
The top 30 apps are shown in Figure 2, along with the number of
users the proportion is based on.
Running, cycling, and walking tracking applications dominate
the top, as well as Spark People, which provides a combination of
weight loss and ﬁtness (although the proportion is based on eleven
respondents). Towards the bottom, we ﬁnd generic health resources
like WebMD, as well as pregnancy apps (I’m Expecting). Thus, we
observe applications with an explicit activity to perform are better at
supporting regular exercise than, say, more generic pedometers or
Effect of Values and Technology Use on Exercise UMAP ’19, June 9–12, 2019, Larnaca, Cyprus
−4 −3 −2 −1 0 1
Figure 1: Coefﬁcients of linear model predicting Health & Fit-
ness app use, with those signiﬁcant at p<0.05 bolded in green.
5 MODELING EXERCISE
Thus, we ﬁnd value determinants in our study subjects’ willingness
to use the health applications, but we are interested in whether
such knowledge would help understand the step to exercise. Indeed,
we ﬁnd that respondents who have downloaded such applications
are 42% more likely to say they exercise (
as we illustrate in the following sections, exercise behavior has a
multifaceted nature beyond health app usage.
5.1 Demographics of Exercise
In this study, we operationalize health-related activities of the user
via the questionnaire, mainly the reply to question “I exercise regu-
larly”, to which a binary yes/no reply is allowed. As a self-declared
assessment of action, the variable suffers from the biases endemic to
surveys, including acquiescence bias (tendency to reply positively),
social desirability bias (tendency to reply in line with perceived
expectations), and faulty recall. Participants may also have a unique
understanding of the frequency of exercise which may be consid-
ered “regular”. Since we are interested in comparing participants
within the study, we make an implicit assumption that the biases and
individual noise are uniformly distributed through the population
(more on this limitation in the Discussion section). In our data, 8,390
(55.8%) of respondents indicated they exercised regularly, the rest –
We begin by examining the basic demographic characteristics
of the two groups, shown in Figures 3. As the plots show the 95%
conﬁdence intervals, we can discern some statistically signiﬁcant
differences in the two groups. Mostly, we see no signiﬁcant age
differences, except for in 34-49 range, when it is slightly more likely
that the users do not exercise. Similarly, there are slightly more males
indicating that they exercise than females. Education and income
prove to be a more discerning feature, with college graduates and
post-graduates exercising markedly more, and high-school graduates
Instant Heart Rate
0.00 0.25 0.50 0.75 1.00
Figure 2: Applications in the Health & Fitness category ordered
by the proportion of respondents reporting exercising regularly,
with the nshown in white.
less. Similarly, the higher income individuals (household income of
$70k or more) reported exercising markedly more than those in the
lower brackets ($50k or less). A similar observation can be made
for the wealth variable, with those having a net worth of less than
$50k reporting to be exercising markedly less than those having
over $250k (plot omitted for brevity). These ﬁndings echo earlier
observed tendency of those in higher income stratum to engage in
higher levels of physical activity (as has been described in a literature
review  and later measured using accelerometers ).
5.2 Predicting Exercise
Next, we would like to determine whether it is possible to use this
data to predict engagement in physical activity. We formulate this
study as a supervised classiﬁcation problem, aiming at predicting
whether participants exercise. We assess the predictive power of:
•Demographics (ethnicity removed due to sparsity)
Psychometry: Moral Foundations and Schwartz Basic Human
Values, and Emotional Contagion
Health-related variables: including replies to survey questions
explicitly about health
•Web domain categories: for both desktop and mobile users
•Application categories: for mobile users only
•Rate of usage of Health & Fitness applications
Note that we chose to single out the demographic and survey
variables having to do with health and health-related attitudes into
their category, as they tend to be highly correlated with exercise.
Focusing only on our “Mobile” dataset for which we have all
the above information (n=2620), we train a Random Forest (RF)
] inferring each time from a richer set of predictors as
presented in Table 2. The choice of the classiﬁer is motivated by
its ability to deal with the sparse web browsing activity data in our
UMAP ’19, June 9–12, 2019, Larnaca, Cyprus Mejova & Kalimeri
< High School
Figure 3: Demographic distribution of respondents, broken down by whether they indicated they exercise, with 95% conf. intervals.
Table 2: Performance of random forest models predicting whether participant indicated exercise, measured using weighted AUROC,
along with p-value of signiﬁcance in difference with Experiment 1 (Basic demographics).
Features included AUROC p-value
1. Basic demographics (only gender and age) .513 -
2. Advanced demographics .616 <0.0001
3. Advanced demographics + values/morals .623 <0.0001
4. Advanced demographics + values/morals + value health .650 0.0001
5. Advanced demographics + values/morals + value health + domains cat-s .654 <0.0001
6. Advanced demographics + values/morals + value health + app cat-s .671 <0.0001
7. Advanced demographics + values/morals + value health + domains cat-s + app cat-s .672 <0.0001
8. Advanced demographics + values/morals + value health + domains cat-s + app cat-s + H&F Time .673 <0.0001
9. Domains cat-s .546 0.07
10. Advanced demographics + domains cat-s .646 0.0002
11. App cat-s .608 0.001
12. Advanced demographics + app cat-s .646 <0.0001
13. Values/morals .575 0.002
14. Values/morals + value health .618 0.0001
dataset, and its performance in previous studies. We perform ten-
fold cross-validation procedure and report the average Area Under
the Receiver Operating Characteristic Curve (AUROC) weighted
statistic over all folds. In the last column of the table, we report
the statistical signiﬁcance obtained by comparing the performance
of each model to the basic demographics baseline. Note that the
random baseline would achieve a weighted AUROC of .50 for all
experiments. For all experiments, our data fusion policy consisted of
“early” fusion at a feature level, concatenating the different feature
vectors for each respondent.
We begin by attempting to predict exercise inferring only on
the most basic demographic attributes – gender and age (Ex. 1 in
Table 2), ﬁnding performance to be not much beyond the random
baseline. However, adding richer demographic attributes, such as
wealth, income, and educational level, signiﬁcantly improved our
prediction to 0.616 (at p<0.001).
Enhancing the baseline model with information about the moral
values (Ex. 3), we note an improvement in the performance. The
increase is even more pronounced - as expected - with the inclusion
of the health-related variables (Ex. 4). Adding web browsing domain
categories (Ex. 5) slightly improved the model, but it is when includ-
ing the categories of the applications used (Ex. 6) that the accuracy
is increased in a notable way to 0.671. Including both sets of fea-
tures - apps and domains categories - (Ex. 7), as well as the average
time people used a Health & Fitness application (Ex. 8) performs
statistically identical to Ex. 6, indicating that the mere knowledge of
application being opened once is enough.
Examining the predictive power of each variable type, we ﬁnd
internet browsing domain categories to be the least useful (Ex. 9,10),
followed by application usage (Ex. 11,12), and the most valuable
(although also most difﬁcult to obtain) the moral values, including
those about health (Ex. 13,14) and advanced demographics (Ex. 2)
including wealth and income.
Thus, we illustrate the utility of value beliefs in modeling ex-
ercise, which combined with demographics and technology usage
substantially outperform the baseline demographics model.
Effect of Values and Technology Use on Exercise UMAP ’19, June 9–12, 2019, Larnaca, Cyprus
Table 3: Logistic regression models predicting exercise using demographic (D), values (V), health (H), url domain (U), and app
(A) features, applied to users who shared PC activity (P), mobile activity (M) or neither (N). Only features signiﬁcant at p<0.01
level shown (before Bonferroni adjustment), alongside their coefﬁcient estimate and their corresponding p-values (now Bonferroni-
adjusted). Conﬁdence levels: p<0.001 ***, p<0.01 **, p<0.05 *.
D (N+P+M) D+V (N+P+M) D+V+H (N+P+M) D+V+H+U (P) D+V+H+U (M) D+V+H+A (M) D+V+H+U+A (M)
n=15021 n=15021 n=15021 n=4995 n=2260 n=2260 n=2260
M F =0.031 R2
M F =0.047 R2
M F =0.074 R2
M F =0.704 R2
M F =0.870 R2
M F =0.868 R2
M F =0.875
(Intercept) -0.6837 *** -1.4740 *** 0.1931 -0.0914 1.1420 0.6129 1.0410
education 0.1334 *** 0.1372 *** 0.1164 *** 0.1313 *** 0.1243 0.1294 0.1239
gender -0.0161 -0.0058 -0.0819 -0.0898 0.1450 0.0729 0.0981
income 0.0561 *** 0.0626 *** 0.0442 * 0.0761 0.0833 0.0729 0.0696
parent -0.1035 -0.1112 -0.1091 -0.0265 -0.3953 -0.3543 -0.4137
wealth 0.1745 *** 0.1709 *** 0.1518 *** 0.1387 *** 0.1714 ** 0.1453 * 0.1743 **
age -0.0795 *** -0.0858 *** -0.0708 *** -0.0630 -0.0441 -0.0052 -0.0126
marital_status_married -0.0766 -0.0712 -0.0793 -0.3038 -0.1720 -0.2266 -0.1229
political_party_vote 0.0253 0.0385 0.0304 0.0396 0.0403 0.0465 0.0507
EC_Happiness 0.0541 *** 0.0162 0.0063 0.0420 0.0479 0.0331
EC_Sadness -0.0269 -0.0207 0.0086 -0.0698 -0.0665 -0.0689
MFT_authority -0.0089 -0.0169 -0.0015 -0.0071 -0.0084 -0.0105
MFT_loyalty 0.0160 0.0244 *** 0.0144 -0.0043 0.0007 -0.0027
SWV_achievement -0.0594 -0.0594 -0.0442 -0.0788 -0.0675 -0.0776
SWV_benevolence -0.1262 * -0.1444 ** -0.0643 -0.1547 -0.1210 -0.1481
SWV_hedonism -0.1418 *** -0.1304 *** -0.1027 -0.1832 -0.1547 -0.1671
SWV_power -0.1655 *** -0.1083 ** -0.1337 -0.1553 -0.1321 -0.1462
SWV_security -0.1871 *** -0.2111 *** -0.1488 -0.1920 -0.1771 -0.1692
SWV_selfDirection -0.0633 -0.0709 -0.0841 0.0099 0.0416 0.0230
SWV_stimulation 0.1131 ** 0.1221 *** 0.1247 0.1581 0.1726 0.1698
SWV_tradition -0.1554 *** -0.1371 *** -0.1389 -0.2205 -0.1608 -0.1980
hypocrisy_increasing -0.0253 -0.0304 -0.0182 -0.0619 -0.0557 -0.0578
blood_pressure_high -0.4244 *** -0.5468 *** -0.4903 * -0.5326 ** -0.5493 *
chronic_disease -0.4653 *** -0.4767 *** -0.3628 -0.3190 -0.3262
smoker -0.2274 ** -0.3870 -0.2631 -0.1437 -0.1423
HQ_1_health_plans -0.1579 *** -0.1065 -0.1080 -0.0718 -0.0847
HQ_4_habit_choice -0.2547 *** -0.3755 *** -0.4382 *** -0.4013 *** -0.4491 ***
HQ_5_health_is_gift 0.0605 ** 0.0473 0.0096 0.0241 0.0326
HQ_6_avoid_test_results 0.0576 ** 0.0801 0.0084 0.0419 0.0138
Education_Reference -0.0040 0.0022 0.0029
General_News 0.0002 -0.0033 -0.0035
Interactive_Web_Applications 0.0477 0.9753 1.1660
Internet_Radio_TV 0.0252 0.1096 0.1118
Malicious_Sites -0.1538 0.0120 0.0103
Motor_Vehicles -0.0143 -0.0341 -0.0353
Online_Shopping -0.0026 -0.0019 -0.0021
Personal_Network_Storage 0.1282 0.3129 0.2914
Personal_Pages 0.0366 -0.3409 -0.3620
Recreation_Hobbies 0.0161 0.0213 0.0229
Search_Engines 0.0014 -0.0015 -0.0017
Sports 0.0061 0.0131 0.0119
total_web_visits 0.0011 -0.0001 0.0014
total_app_time 0.0000 0.0000
ENTERTAINMENT 0.0011 0.0012
HEALTH_AND_FITNESS 0.0095 *** 0.0095 ***
HOUSE_AND_HOME -0.0137 -0.0110
LIBRARIES_AND_DEMO -0.5137 -0.5396
LIFESTYLE 0.0051 0.0047
MAPS_AND_NAVIGATION 0.0052 0.0054
MEDICAL -0.0168 -0.0162
MUSIC_AND_AUDIO 0.0037 0.0038
5.3 Determinants of Exercise
In the aim of understanding the contribution of individual variables
to whether a person exercises, we employ multivariate logistic regres-
sion analysis, building models from demographics-based baseline.
The resulting models are shown in Table 3. In consideration of space,
only variables which have a coefﬁcient signiﬁcant at
shown; note however that the p-value markers (stars) shown in the
table have been Bonferroni-adjusted to ameliorate the multiple com-
parison problems. Intuitively, the Bonferroni correction “punishes”
the signiﬁcance of features in a larger model, allowing fewer oth-
erwise signiﬁcant tests pass the adjusted
threshold (as is visible
in right-most columns of the table). Also at the top of each model,
we show the number of users having non-empty ﬁelds available for
the data (
) and McFadden’s
, which relates the (maximized)
likelihood value from the current ﬁtted model to null model .
In baseline model using only demographics we ﬁnd a strong
positive relationship between exercise and education,income, and
wealth, and a negative one with age (with age echoing ﬁndings of
UMAP ’19, June 9–12, 2019, Larnaca, Cyprus Mejova & Kalimeri
previous studies [
]). However, the explanatory power of this
model is low, according to
. Adding the moral values, we ﬁnd
exercise to be highly related with happiness and stimulation, and
negatively related to hedonism,power,security, and tradition. Note
the difference between these and values associated with download-
ing a health app from Section 4, now with an negative association
with power (“attainment of a dominant position”) and positive with
happiness (latter is well documented to accompany exercise ).
Next, the addition of health-related variables unsurprisingly pro-
duces highly signiﬁcant coefﬁcients. There is a robust negative re-
lationship between exercise and having high blood pressure, some
other chronic disease, and being a smoker. Interestingly, we also ﬁnd
strong effects in the belief statements of respondents. Those who
exercise are more likely to agree with the statement “When I think
of making plans for the future, my health is something I strongly
consider” (HQ 1), “We all have a choice about how to lead our lives,
and healthy habits are just one example of that” (HQ 4), but are more
likely to disagree with “Health is a gift and there is not much I can
do about it” (HQ 5) and “Sometimes I avoid getting my test results
if I think it will be bad news” (HQ 6). These ﬁndings underscore the
importance of the individual’s belief in their innate ability to achieve
goals or self-efﬁcacy .
Upon adding the internet browsing data (available for both Desk-
top and Mobile cohorts), we ﬁnd the most important domains to be
in the areas of Personal Network Storage (content management),
Malicious Sites (may include adult content), and Interactive Web
Applications (document readers, calendar), though none had a par-
ticularly signiﬁcant p-value after Bonferroni correction. Though in
combination with baseline variables, the model achieves
0.704 and 0.870 for Desktop and Mobile cohorts respectively.
Finally, as we consider the use of applications (listed in caps) for
the respondents who shared their mobile activity, we ﬁnd several
application classes beneﬁcial to the model, the most signiﬁcant
of these being Health & Fitness. Note that we have also included
aggregate technology usage statistics, including a total number of
web visits in the observed time, the total app time and (not shown due
to insufﬁcient signiﬁcance) the total web browsing time. However,
we ﬁnd these to be not highly related to exercise behavior.
As the models increase in complexity (see the last two columns
of Table 3), the Bonferroni correction becomes more strict, which
reveals the variables most important in modeling exercise: a combi-
nation of demographics (wealth), attitude (HQ4 “Health is a choice”),
and technology use (Health & Fitness Apps).
6 DISCUSSION & CONCLUSIONS
This study is a contribution to an exciting area of research in User
Modeling and Personalization into the user-centered design of per-
suasive technologies and behavior change interventions to improve
health and well-being. In the past, psychological features have been
used to model healthy shopping habits [
], cooking [
], and engage-
ment in physical activity [
]. Thus far, existing UMAP literature
focused on personality traits which could be leveraged for personal-
]. Here, we show that values held by the individuals
also affect their health behavior. In a sense, it can be seen as a re-
sponse to a recent study on strategies to encourage diet and physical
exercise by Radha et al. [
], who called for the study of factors that
could “explain attitude towards the feasibility level of a recommen-
dation”. Here, we illustrate the use of validated, ﬁner-grained value
theories in the modeling of technology users, while also contributing
an observational technology use and rich demographics.
In particular, insights obtained in this study lead us to recommend
the following considerations when designing persuasive technologies
for health behavior modiﬁcation:
Focus on a
to track. We show that applications
having most users exercising are marketed for tracking a particular
activity, such as walking, running, or cycling. Those centered
around particular wearables, for instance, fare less well.
Create interventions with
of the users in
mind. We ﬁnd that wealth is one of the greatest determinants of
regular exercise. More should be done to understand the barriers
of the less wealthy to leading a healthy lifestyle. For instance, it is
curious that it is wealth, not income, that remains most predictive
in the regression model.
Incorporate discussion of
in the interaction or interface.
One of the strongest predictors for exercise is the belief that
healthy habits are a conscious choice. Making this choice explicit
may reinforce this value and encourage engagement.
Encourage the expression of
and offer emotional re-
wards. We ﬁnd people engaging in exercise identifying more
strongly with the value of happiness, and although the direction
of causation may point both ways, associating positive emotions
with physical activity may reinforce the connection. Note that
our ﬁndings that the value of power is negatively associated with
exercise suggest that competitions and leader boards may not be
appropriate for many users.
Use application usage in
, in the absence of
detailed demographic or value information. In our classiﬁcation
experiments, we ﬁnd mobile application usage to be much more
useful in predicting exercise than internet browsing.
As mentioned earlier, the greatest limitation of this study is the
reliance on self-reporting when measuring exercise, as many biases
are possible. In future studies, we encourage researchers also to
obtain permission to gather user physical activity (which can be done
unobtrusively via pedometers and heart rate monitors). Further, we
realize administering scientiﬁcally validated surveys to technology
users may be infeasible. However, attempts are being made to detect
moral judgments in social media [
] and associated with images
], with potential for automatic value detection in the future. Also,
although we capture one month of technology use, the study is not
longitudinal – many people may have downloaded and used the
apps at some time, but not in our window of observation. Thus
conclusions on application adoption/retention should be made while
considering the small eventual sample size per application.
Finally, we would like to reiterate the privacy precautions taken
in this study, with respondent anonymization, data aggregation, and
URL cleaning, which is performed to limit the exposure of partici-
pants. Similar precautions should be taken if or when an inference
of values or other personal information is performed, such that the
user is given greatest possible control over his or her information, as
enforced by, for instance, EU General Data Protection Regulation
Effect of Values and Technology Use on Exercise UMAP ’19, June 9–12, 2019, Larnaca, Cyprus
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