Conference PaperPDF Available

Quantifying the Quantified Self: A Study on the Motivations of Patients to Track Their Own Health.

Authors:

Abstract and Figures

A new generation of patient-driven healthcare information systems (HIS) is emerging to advance traditional healthcare services and empower patient self-responsibility. Pro-fessional approaches to develop or improve HIS exist alongside evolving individual and community-shared approaches where patients take responsibility for their health data and health. Health Social Networks and the Quantified Self community are examples for such patient-driven initiatives. They inherently focus on empowering self-determination and responsibility. The success of future HIS relies – at least partially – on their engi-neers’ and developers’ capability to understand and use impulses from their respective target groups. The present study on self-tracking motivations aims to shed light on what drives people to track themselves. To this end, we conducted an exploratory survey with 150 self-trackers and developed a Five-Factor-Framework of Self-Tracking Motivations. The framework includes an inventory of five factors and a psychometrical scale of 19 items to measure individual drivers for self-tracking.
Content may be subject to copyright.
Thirty Fourth International Conference on Information Systems, Milan 2013 1
QUANTIFYING THE QUANTIFIED SELF:
A STUDY ON THE MOTIVATION OF PATIENTS TO
TRACK THEIR OWN HEALTH
Completed Research Paper
Henner Gimpel
Marcia Nißen
Karlsruhe Institute of Technology (KIT)
Institute of Information Systems and Marketing
Englerstr. 14, 76131 Karlsruhe, Germany
henner.gimpel@kit.edu
marcia.nissen@student.kit.edu
Roland A. Görlitz
FZI Research Center for Information Technology
Haid-und-Neu-Str. 10-14, 76131 Karlsruhe, Germany
goerlitz@fzi.de
Abstract
A new generation of patient-driven healthcare information systems (HIS) is emerging
to advance traditional healthcare services and empower patient self-responsibility. Pro-
fessional approaches to develop or improve HIS exist alongside evolving individual and
community-shared approaches where patients take responsibility for their health data
and health. Health Social Networks and the Quantified Self community are examples for
such patient-driven initiatives. They inherently focus on empowering self-determination
and responsibility. The success of future HIS relies at least partially on their engi-
neers’ and developers’ capability to understand and use impulses from their respective
target groups. The present study on self-tracking motivations aims to shed light on
what drives people to track themselves. To this end, we conducted an exploratory sur-
vey with 150 self-trackers and developed a Five-Factor-Framework of Self-Tracking
Motivations. The framework includes an inventory of five factors and a psychometrical
scale of 19 items to measure individual drivers for self-tracking.
Keywords: healthcare information systems, health informatics, medical information
systems, psychology, survey research, service science, service engineering
Healthcare Information Systems
2 Thirty Fourth International Conference on Information Systems, Milan 2013
Introduction
In recent years, the possibilities of keeping records regarding daily activities, exercises, vital parameters,
disease symptoms, nutrition, and much more increased remarkably due to new information technology,
decreasing sensor size, and increasing smartphone usage. Consequently, the idea of garnering knowledge
about oneself by quantifying and analyzing self-related data attracts an ever growing community of self-
trackers.
In 2007, with the release of the first iPhone, a group of patients and individuals started self-tracking and
recording data of their daily life by the help of smartphones and other technical devices within the scope
of a philosophy they termed Quantified Self QS for short (Kelly et al., 2007). QS members do not only
create personalized healthcare data sets by collecting and analyzing their personal lives with apps and
(partly self-developed) technical body measurement devices, they also share their own needs, develop-
ments, and ideas of individualized medical treatments in online and real-life meetings. They generally aim
at optimizing their health, performance, or every-day life with a chronic disease by analyzing statistics and
using new technologies all self-motivated, voluntarily, and autonomously (Wolf, 2008).
At the same time, healthcare professionals, academics, and policy makers attempt to transform healthcare
delivery towards patient empowerment and involvement. For example, novel IT-based records such as
electronic medical records, electronic patient records, and personal health records are increasingly used
(Mandl and Kohane, 2008). Though discussed controversially, healthcare information systems (HIS) a
frequently seen as having the potential to reduce healthcare costs and improve outcomes in many applica-
tion areas see Fichman et al. (2011) and Black et al. (2011) for overviews. Particularly, in primary care
the improvements are not fully validated (Bélanger et al., 2012). In remote health monitoring (RHM),
however, the potential is already exploited and patients living at home may benefit from these technology-
driven changes in healthcare delivery (Singh et al., 2011). The potential of HIS attracts healthcare profes-
sionals, academia, patients themselves and, more generally, health-oriented individuals. This individual
awareness and willingness to manage one’s own health opens up new perspectives that need to be consid-
ered from the professional’s perspective.
These new perspectives include exploiting existing data sets and obtaining new data more easily. On the
one hand, any data that patients have already been collecting voluntarily might be used to treat a disease
or monitor its progress without additional effort. The existing data can also be further distributed to other
healthcare stakeholders, for example, the involved therapists and care-giving relatives. On the other hand,
many outpatient treatments, like RHM, rely on patient participation and ambulatory assessment (AA).
Here, addressing “self-tracking traits” might provide better results in terms of secondary prevention or
monitoring data quality. Even though the QS movement has become a phenomenon of health-conscious,
achievement-oriented and self-efficient individuals around the world (TheEconomist, 2012), a profound
understanding of these self-trackers, their activities, and their motivations is lacking. Therefore, we con-
ducted a study among self-trackers to shed light on their profiles, activities, and motivations for self-
tracking. We were guided by the research question: What are the underlying motivations of self-triggered
health monitoring?
This paper presents the foundations for and the findings of the study among self-trackers. Following the
scale development methodology described by Hinkin (1998), qualitative research, pre-tests, and an online
survey with 150 self-trackers led to the development of a profound psychometric scale and framework of
five motivational factors. This framework is meant to help understanding self-tracking motivations in
future research. In addition, the framework might become a tool for developers of HIS, RHM, and AA
systems systems that help garnering data of patients in real-life situations and in real-time (Trull and
Ebner-Priemer, 2012) in their aim to design and provide systems and services that are widely accepted
by patients. With this, we are contributing to the research agenda put forward by Agarwal et al. (2010) in
extending the traditional realm of health IT and supporting HIS design.
Related Work
There is a wide field of literature regarding healthcare information systems, e.g. on the management of
health information across computerized systems and its secure exchange (Haux, 2006; Kelley et al., 2011).
The majority of these approaches introduce IT to enhance existing healthcare delivery processes and in-
Gimpel et al. / Quantifying the Quantified Self
Thirty Fourth International Conference on Information Systems, Milan 2013 3
formation exchange between healthcare professionals. Such an exchange of information between
healthcare professionals is out of scope for this paper. It targets rather patient-centered health infor-
mation systems including data generated by patients that track themselves. As described by Paré et al.
(2007) in their systematic review, even the more patient-centered approaches rather statically collect
information in medical records. These records can be enriched by the contribution of real-time health
information and extensive measurements collected continuously by the patients themselves (Shin, 2012;
Trull and Ebner-Priemer, 2012). One first step towards achieving this goal is provided by online health
communities, in which patients share and discuss their health information (Eysenbach, 2008). Connect-
ing the patient-initiated tracking data to health information systems majorly used by physicians is not yet
accomplished (Singh et al., 2011).
Physician-initiated tracking. Several approaches exist where physicians ask their patients to track
their health and make the information available to the physician. Remote Health Monitoring (RHM),
sometimes referred to as Remote Patient Monitoring or Telemonitoring, presupposes a physical distance
between patient and physician that inherently impairs classical healthcare (Paré et al., 2007). RHM com-
prises all professional activities that help redressing this distance by monitoring typical vital, physiologi-
cal, and biological data, e.g., at a patient’s home by the aid of static, mobile, or wearable sensors and
devices. The automated or manual transmission of all garnered data to the physician in charge intends to
ensure high quality healthcare (Schmidt et al., 2010). The aim is to provide more regular and continuous
information on vital signs which has the potential to reduce acute exacerbations” (Trueman, 2009). Per-
manent or temporary monitoring of patients in their typical environment has been found applicable espe-
cially for chronic diseases such as adiposity, hypertension, diabetes, respiratory conditions, or chronic
heart failure. Particularly for the latter three, RHM’s positive effects regarding patient empowerment and
health have been reported multiple times (Chaudhry et al., 2007; Jaana and Paré, 2007; Jaana et al.,
2009; Polisena et al., 2010).
Closely related to RHM is the notion of Ambulatory Assessment (AA). AA refers to patients garnering data
in real-time in their natural environment (Trull and Ebner-Priemer, 2012). It promises “to minimize ret-
rospective biases while gathering ecologically valid data, including self-reports, physiological or biological
data, and observed behavior, for example, from daily life experiences” (Trull and Ebner-Priemer, 2012).
AA therefore focusses on (semi-)automated, fast accessible and IT-based systems that help collecting data
in real-time and real-life constraints (Ebner-Priemer and Kubiak, 2007). Health records and analytic sys-
tems help assessing physiological and vital parameters under everyday conditions, inter alia, the electro-
cardiogram (heart rate), blood pressure, respiration, skin temperature, and movements (Fahrenberg et
al., 2007).
RHM and AA are prominent for but not limited to chronic diseases. They are additionally applied for pre-
ventive healthcare. A wide range of portable data recording systems has been developed that consider
environment and social interactions, special habits, and mood changes (Mehl and Holleran, 2007).
Patient-initiated tracking. In addition to physician-initiated tracking, more and more people become
self-responsible for their health and start tracking themselves without a healthcare professional sug-
gesting data collection, they start quantifying themselves out of intrinsic motivation. Patient-initiated
tracking and information management, such as Health 2.0 (Eysenbach, 2008), Health Social Networks
(Eysenbach, 2008; Free et al., 2010; Istepanaian and Zhang, 2012; Swan, 2009), and mHealth (Free et al.,
2010; Istepanaian and Zhang, 2012) can add valuable health information for usage by patients themselves
and by health professionals.
Health Social Networks, like PatientsLikeMe
1
and CureTogether
2
, form a special type of social network
that besides being an online community accumulate the members’ vital parameters such as body
weight, height, blood pressure, heart rate, and much more. This new class of patient-driven healthcare
services is emerging to supplement and extend traditional healthcare delivery models and empower pa-
tient self-care (Swan, 2009, p. 492). The term self-tracking thereby refers to all actions of regular, volun-
tary elicitation and collection of all kind of metrics that can be related to a person. These metrics consist of
daily-life parameters covering health, well-being, fun, behavioral, or environmental aspects of their lives
1
www.patientslikeme.com, retrieved May 1, 2013.
2
www.curetogether.com, retrieved May 1, 2013.
Healthcare Information Systems
4 Thirty Fourth International Conference on Information Systems, Milan 2013
(Swan, 2009, p. 509). Examples relevant to healthcare include tracking of controlled or reflected health
conditions, disease symptoms, medication, body weight, sleep quantity and quality, blood test results,
blood pressure, nutrition, habits, and mood.
When patients track their lives, their behavior, or their habits, they “quantify” themselves. The term of
Quantified Self (QS) evolved in 2007, when the American journalists and publishers of The Wired Maga-
zine, Gary Wolf and Kevin Kelly, founded the blog QuantifiedSelf.com, which remains the most important
website and central hub within the QS Community till today (TheEconomist, 2012). Today, the term
Quantified Self addresses two dimensions of this movement. First, it includes the idea of garnering
knowledge about oneself by quantifying and analyzing self-related data as it is declared in the credo self-
knowledge through numbers” (Wolf, 2008). Second, people that keep records of certain aspects of their
lives form a worldwide connected community under the name “Quantified Self” (Butterfield, 2012). This
community as an entity is connected via the central website QuantifiedSelf.com, via official and private
blogs, in social networks as in Facebook groups, but also in real-life so-called Meetup Groups and re-
gional and global conferences (Kelly et al., 2007). Meetup.com reports about 20.000 self-trackers
3
. Fox
and Duggan (2013) estimate that already 69% of U.S. adults track health indicators for themselves or
loved ones and one in five thereof uses technology to do so.
Due to its relative novelty, QS and health social networks especially their members’ motivations to par-
ticipate in self-tracking activities are not yet well understood. Only few scientific papers study the phe-
nomenon. The most prominent contribution so far was made by Swan (2009), who examined “Emerging
Patient-Driven Health Care Models”, specifically Health Social Networks, Consumer Personalized Medi-
cine, and Quantified Self-Tracking. Swan reviews the field of patient-driven healthcare models qualita-
tively, identifies trends, and touches upon potential challenges. Building on her work, we proceed
quantitatively by building a psychometric scale and framework to understand the motivations of patients
to engage in patient-driven healthcare models.
To the best of our knowledge, so far no research has been published on motivational aspects and psycho-
metric scales for self-tracking of health data.
Since the major challenges and research areas of both physician- and patient-initiated approaches are not
merely technology but rather acceptance, compliance, privacy, and potentially sustainable business mod-
els (Trull and Ebner-Priemer, 2012) considering the intrinsic motivation of self-trackers may lead to more
effective and efficient healthcare information systems and patient empowerment.
Research Methodology and Procedure
In order to understand the underlying motivations of self-triggered health monitoring, we conducted a
structured survey among self-trackers. Specifically, we followed the methodology described by Hinkin
(1998) with six consecutive steps: (1) item generation, (2) survey administration, (3) initial item reduc-
tion, (4) confirmatory factor analysis, (5) further construct validity assessment, and (6) replication. The
last step is left for future research.
Step 1: Item generation. The construct of interest is the motivation of self-triggered health monitoring.
To articulate the theoretical foundations of this construct, we proceeded deductively and inductively. We
combined logical partitioning via a broad based review of extant scientific literature especially focusing
Quantified Self, on positive and motivational psychology, online communities, and social media. In addi-
tion, we grouped information from self-trackers, found on Web pages and blogs and, most importantly, in
a series of semi-structured face-to-face expert interviews with members of the QS community (Hinkin,
1998).
With regards to literature specifically on Quantified Self, the following sources proved most valuable for
our study: Swan (2009), Kelly (2007), Wolf (2008, 2010), Butterfield (2012), and Fox and Duggan (2013).
Examples for the literature on positive and motivational psychology that was incorporated in our study
include Csikszentmihalyi and Csikszentmihalyi (1975), Csikszentmihalyi (1997), and Levesque et al.
3
http://quantified-self.meetup.com/all/, retrieved May 1, 2013.
Gimpel et al. / Quantifying the Quantified Self
Thirty Fourth International Conference on Information Systems, Milan 2013 5
(2010). On online communities and social media, examples for the literature reviewed are Ludford et al.
(2004), Tedjamulia et al. (2005), Eysenbach (2008), and Preece and Shneiderman (2009). As this paper
does not report a systematic literature review but survey research, this list of references is not exhaustive,
but only serves as example to give the reader a taste of the different streams of research that were
considered when looking for theoretical foundations and potential scales and items to include in our
study.
According to Swan (2009), many different fields of self-tracking have been identified comparing various
self-trackers’ personal blogs (e.g. http://measuredme.com/, retrieved May 1, 2013) and recorded experi-
ence reports from the official Quantified Self website (http://quantifiedself.com/topics/videos/, retrieved
May 1, 2013). This was complemented by news reports like The Economist (2012).
Scientific literature, Web pages, blogs, and news were used to first build a mind map of relevant topics in
the context of self-tracking, e.g. parameters tracked, technologies used, motivations and hurdles for track-
ing and the like. In this process, scientific literature was used deductively, Web pages, blogs, and news
inductively. The mind map was then used to draft an interview guide for semi-structured interviews along
the structure sketched in Table 1. Six interviews were conducted face-to-face at the 4th Meetup of the
Quantified Self Berlin Meetup Group in September 2012. Three interview partners had been holding stage
presentations on their past self-tracking experiences during this event; the other three were attending the
Meetup as regular participants. Interviews were transcribed and analyzed by the research team to extend,
detail, and prioritize the mind map. This process resulted in a set of constructs and sub-constructs and a
preliminary list of potential items that characterize these.
Table 1. Structure of the interview guide for expert interviews
Underlying
question
Explanation
1.
Welcome and screen-out: Is the respondent an actual self-tracker or not?
2.
“What?”
Are the participants either tracking well-being or health-related parame-
ters or both or not directly self-related? Which parameters are they
tracking concretely? Do they maybe suffer from a chronic disease?
3.
“How much?”
How much time do self-trackers spend on self-tracking activities? For
how long are they already self-tracking?
4.
“Why?”
What are the underlying and psychological motivations to include self-
tracking activities in their daily lives?
5.
“Why not (more)?”
What could be possible hurdles for self-trackers to keep on self-tracking?
6.
“How?”
How to self-trackers measure and record their life? Do they use specific
tools and spend a lot of money on it or are they trying to keep it as simple
as possible?
7.
“Who?”
Implies being a self-tracker several specific characteristics of the person-
ality’s dimensions?
8.
“Who?”
Standard questions on the demographic background of the participants:
age, origin, income, occupation.
9.
Did you answer honestly? Are you paying attention? Do you want to
participate in the lottery drawing for one out of three Amazon vouchers?
At this stage, the structure for the survey was developed and literature was screened for existing scales
and items. To assess personality, for example, the short version of the Big Five Personalities Tests (BFI-
10) has been added (Rammstedt and John, 2007). On the motivations the core of the present study
there was no applicable scale readily available. Items were newly developed, following standard guidelines
(e.g. Hinkin 1998). Conceptual and content validity of the preliminary items were assessed in two ways:
First via peer-review by researchers from information systems and psychology (different from the re-
search time) and, second, in a pre-test as suggested by Schriesheim et al. (1993), where items were admin-
istered to 20 self-trackers.
Based on this validity assessment, some items were changed, re-phrased, or dropped. A set of 31 items on
motivation emerged, each scaled on a 5-point Likert-type scale labeled disagree strongly, disagree a lit-
tle, neither agree nor disagree, agree a little, agree strongly. A list of 31 items is not parsimonious; how-
Healthcare Information Systems
6 Thirty Fourth International Conference on Information Systems, Milan 2013
ever, an extensive list of items with proven content adequacy is beneficial for subsequent item reduction
(Hinkin, 1998; Schriesheim et al., 1993).
Step 2: Survey administration. The items were administered to self-trackers in form of a structured
online survey in English language during November 2012. Besides the items on motivations for self-
tracking, the survey featured questions regarding the extent of self-tracking, the parameters tracked,
technology used, demographics and personality traits of respondents. In addition, screening and control
questions were employed to check validity of responses. The screening question assured that only re-
spondents stating they would be tracking themselves were allowed to enter the survey. The first control
question inspired by the Instructional Manipulation Check (Oppenheimer et al., 2009) had a 5-point
scale and read “If you are paying attention to this survey, please check the second box from the right that
reads Paying Attention’”. The second control question read Did you answer honestly throughout the
questionnaire? Yes or No”. Respondents could answer the survey at their own pace; on average it took
them 14.9 minutes (standard deviation 10.9).
Respondents were recruited offline and online via multiple channels: in-person Meetups related to self-
tracking, Meetup.com online groups related to self-tracking, Facebook groups related to self-tracking and
health-related community groups in general, Twitter with tracking-related hashtags and accounts. In
these channels, the research team distributed the link to the online survey, asked for help in the, research
and announced that three 50$ Amazon gift certificates would be raffled off among survey respondents
who chose to provide their e-mail address (which was not mandatory, one could participate anonymously;
e-mail addresses were not used to identify individual respondents). In addition, we asked self-trackers to
support us in viral marketing: To send the survey link to their friends, post it in official and private blogs
related to self-tracking, re-tweet it and the like. Thus, it is not possible to say who received an invitation
and, hence, self-selection in participating in the survey cannot be measured. This recruitment procedure
is prone to sampling bias. This is one of the key limitations of the present study. We cannot assure that the
sample of respondents is representative for either the entire population of self-trackers or for the set of
people who received an invitation to the survey. Given recruitment procedures, one might especially ex-
pect that the sample is geared towards younger, Internet-focused, community-oriented respondents.
However, a representative sample cannot be assured with reasonable effort, as there neither is a full list of
self-trackers, nor a random sampling procedure in this population or a clear overview on typical charac-
teristics of self-trackers to compare against our sample.
A total of 411 respondents followed the link to the survey. 224 of them answered the screen-out question
Are you keeping records of or tracking anything that occurs in your life? Yes or No” and indicated they
would be self-tracking. 167 of them completed the entire survey (75% of screened respondents), the other
25% dropped out for unknown reason it can be speculated that the survey became too tedious for them,
they might have been interrupted by other activities, or experienced problems such as a loss of internet
connection. From the partial data we obtained from these 57 dropped-out respondents, there is no reason
to suspect any systematic bias related to the survey’s content that caused the dropout.
Finally, 150 of the 167 complete respondents were attentive and honest according to the two control ques-
tions (67% of screened respondents). Data from these 150 respondents is analyzed. 150 observations are
not excessive but sufficient for the following analyses (Guadagnoli and Velicer, 1988; Hinkin, 1998).
Step 3: Initial item reduction. We employed an exploratory principal component analysis (PCA) with
orthogonal Varimax rotation to analyze the structure of the items and reduce the number of items for the
final scale. The number of components to be extracted was determined to be 5 based on a parallel analysis
(Horn, 1965).
4
Following Hinkin (1998), items were iteratively dropped when they had no major loading
(0.4) on any component, low communality (<0.4), major cross-loadings (0.4), or a lack of content fit
(one single item dropped for this last criterion). The parallel analysis was re-run on the reduced set of 19
items and confirmed the existence of 5 principal components. The resulting structure explains a total of
4
Parallel analysis is generally seen as one of the best component extraction methods, particularly it is assumed to outperform the
Kaiser-Guttmann eigenvalue greater one criterion and the scree test employed in some studies (Hayton, 2004). Parallel analysis
generates random data following the same structure as the original data (sample size and number of variables) and numerically
establishes typical eigenvalues from the random correlation matrices. Components in the sample data with eigenvalues greater than
eigenvalues from random data are retained.
Gimpel et al. / Quantifying the Quantified Self
Thirty Fourth International Conference on Information Systems, Milan 2013 7
63% of item variance, exceeding the required target of 60% (Hinkin, 1998). Convergent validity of con-
structs was assessed using Cronbach's alpha (Nunnally, 1978). All components exceed the respective re-
quired target of 0.6 for exploratory studies and new scales (Robinson, 1991). Therefore, the constructs
appear to have adequate internal consistency and possess content validity. Details are reported in the
following section, especially Table 1.
Step 4: Confirmatory factor analysis. To further support the internal consistency of the scale, we
conducted a confirmatory factor analysis on the same data set. The combination of an exploratory princi-
pal component analysis (PCA) and a confirmatory factor analysis might lead to confusion the rationale
is as follows: Our study is exploratory. For the field of quantified-self, there is not yet a strong theoretical
foundation and this study aims at providing a building block for having such a theoretical foundation in
the future. Thus, the exploratory PCA guides our analysis of the motivations for self-tracking. However, a
PCA is not fully able to quantify the goodness of fit for the resulting structure (Hinkin, 1998; Long, 1983).
The confirmatory factor analysis restricts each item to load only on a single component. The analysis is a
confirmation that the prior analysis have been conducted thoroughly and appropriately(Hinkin, 1998).
We used a reflective specification of the model. Both local and global quality criteria are satisfactory. Spe-
cifically, each item loads significantly on its component (at 1% level). 2 divided by the degrees of freedom
is 1.9 and, thus, less than the acceptable limit of 2 (Byrne, 1989, p. 55) or 5 (Browne et al., 1993, p. 144).
The root mean square error of approximation (RMSEA) is 0.078 and, hence, below the acceptable limit of
0.08 (Browne et al., 1993, p. 144). As suggested by Hinkin (1998, p. 115), up to this point, we “can be rela-
tively assured that the new scales possess content validity and internal consistency reliability.”
Step 5: Further construct validity assessment. Criterion-related validity and discriminant validity
were further assessed by relating the motivations to the intensity and object of tracking and by testing for
correlations between motivations and personality traits. Results further strengthen the validity of the
scales. Details are reported in the following section, especially Table 3 and Table 4.
Step 6: Replication. To date, the procedure relies on a single sample. The replication with an inde-
pendent second sample will enhance external validity and generalizability (Hinkin, 1998; Stone and Stone
Eugene, 1978). This step is left for future work and other researchers in order to rule out researcher bias.
Results of the Exploratory Survey
Descriptives
The characteristics of our sample are as follows (n=150): Age ranges from 14 to 76 years with mean 34 and
median 30 years. 71% of respondents are between 20 and 40 years old. 58% are male, 37% female (5% did
not disclose their gender); 41% are employed, 33% students, 17% self-employed, 9% other; 53% are from
Europe, 39% from North America, 8% other.
The objects of tracking within the self-trackers differ in many ways. Some self-quantifiers suffer from
chronic diseases and therefore keep records of their medication or occurring symptoms in different situa-
tions. Others are only tracking their daily steps or the running distances; still others are regularly record-
ing their body weight or body mass index (BMI) and try to correlate it with environmental factors such as
weather or GPS-tracked location. Respondents track 1 to 39 parameters (mean 9, median 8), mainly on
physical activity (e.g., exercises, steps), body (e.g., weight, heart rate, blood pressure), well-being (e.g.,
sleep time and quality, mood), nutrition (e.g., calories intake and balance, water consumption), and medi-
cal issues (e.g., symptoms of chronic diseases, blood-test results, medication).
One third of our respondents suffer from a chronic disease. Out of these, 73% disclosed their diseases
verbally in their response to an open-ended question. These chronic diseases are mainly rheumatoid ar-
thritis, diabetes, and thyroid disorders.
Concerning the question, why they started tracking in the first place, 56% agreed that “they just thought
they should” start self-tracking. Only 28% have heard about QS before, 7% are influenced by friends
that started doing so as well and 8% have been asked by their physician to start tracking some of their
Healthcare Information Systems
8 Thirty Fourth International Conference on Information Systems, Milan 2013
vital parameters or symptoms.
5
Comparing respondents that have started self-tracking before 2010 and
after 2010
6
clearly indicates that respondents, that started tracking before QS became popular in the me-
dia, are less influenced by friends or news but are more likely to have being recommended conducting
self-tracking by their physician ( test; df = 4; p = 0.042). On the other hand since 2010 there have been
more people that have heard about self-tracking in the news: 21% after 2010 compared to 7% of all re-
spondents before 2010.
According to expert interviews with researchers and members of the QS community, these data are by and
large in line with the common perception of self-trackers. Thus, while acknowledging the recruitment
potential bias, we see the survey results as valuable, exploratory contribution in a field lacking research.
Five-Factor Framework of Self-Tracking Motivations
The main aim of this study is to understand the deeper underlying motivations of self-tracking: What
exactly keeps a self-tracker working on his tracking activities? Is he or she rather driven by the fun and
entertaining aspects of self-tracking or rather by a strong self-responsibility and willingness to reach per-
sonal goals?
A self-tracking motivation model with five motivations has been developed with the help of an exploratory
principal component analysis (PCA) on responses from 150 self-trackers to an online survey. The model
includes a set of 19 question items by which the motivational aspects of Self-entertainment, Self-
association, Self-design, Self-discipline, and Self-healing on self-tracking can be measured. Table 2 pro-
vides key statistics on the results of the PCA.
Factor 1: Self-entertainment motivated due to the “pleasure-bringing” aspects of self-tracking
The first component of the PCA deals with the enjoyment, fun, and ludic aspects of self-tracking. The mo-
tivation by Self-entertainment refers to the fun of one’s preoccupation with a technical device or the en-
joyment of playing around with numbers and statistics of one’s own-related data. Some of the items refer
to the state of flow as described by Csikszentmihalyi and Csikszentmihalyi (1975), a mental state charac-
terized by the experience of forgetting about time and getting lost in the respected activities
(Csikszentmihalyi, 1997).
The questionnaire contained an open-ended question for the respondent’s motivation prior to suggesting
motivations and asking for their applicability in closed Likert-type questions. Answers to this open-ended
question further illustrate the factors they include the following: “I like mapping and data. I collect my
own data so I can play around with it” (Participant #277), and I have always been curious about meas-
uring everything even without any special purpose. I found that you learn a lot from data even when
you were not searching anything special. I like QS for QS sake, not just for solving problems (Partici-
pant #403).
Factor 2: Self-association motivated by the prospect of community citizenship and self-
individualizing aspects within a community
The second factor mainly differs from the other factors as this has less to do with one’s self but with one’s
relation towards a community and with others. The term Self-association is a combination of the words
association (which can concurrently mean community, affiliation and resemblance) and self. This motiva-
tion comes from the idea, that there is no individuality without community; every self-tracker needs a
counterpart to understand him- or herself mainly by comparison. Thus, this factor does not necessarily
mean that a self-tracker needs the community to satisfy a certain sense of belonging but rather implies a
self-tracker’s need to understand his individuality within a respective environment. Future HIS should
5
From data analysis, we have no indication that self-trackers triggered by their physician are different from self-trackers without
physician stimulus. Thus, we pool the data. This can be seen as support for generalizing results to physician-initiated tracking. How-
ever, the sample of respondents with physician-initiated tracking is too small to statistically assert equality of the groups.
6
In 2010, the first journalistic reviews of the Quantified Self movement in the US can be found on the Internet. Thus, it might be an
interesting inflection point to distinguish people starting self-tracking before or after the media hype.
Gimpel et al. / Quantifying the Quantified Self
Thirty Fourth International Conference on Information Systems, Milan 2013 9
consider patientsinterest in comparing their results to others. They also should include functions that
serve today’s patients’ need to present themselves and their belief to be able to help and/or inspire others.
Participant #310 responded he would be self-tracking to be able to see my personal evolution and the
activities of my friends or people who have the same interests”.
Table 2. Rotated component matrix of the exploratory principal component analysis
(Varimax rotation with Kaiser normalization; major loading in bold font, cross-loadings in grey font)
I’m self-tracking because
Factor
Commu-
nality
Cronbach’s
alpha
1
2
3
4
5
Self-entertainment
... I enjoy getting lost totally in self-tracking activities. (#01)
.77
.06
.11
-.12
.31
.71
.78
... I like playing around with numbers/statistics etc. (#02)
.73
.13
.16
.06
-.08
.59
... I like playing around with my smartphone/technical device etc. (#03)
.72
.11
.24
.14
-.19
.64
... I enjoy forgetting about time while doing so. (#04)
.68
.04
.00
-.12
.34
.59
... it is fun and entertaining. (#05)
.60
.22
-.09
.34
-.07
.54
Self-association
... I want to help/inspire others. (#06)
-.01
.85
.11
.1
.12
.76
.82
... the way I'm doing it is interesting for others/might help others. (#07)
.18
.83
.19
-.06
.11
.77
... I want to compare my results to others. (#08)
.15
.74
.00
.25
.03
.64
... I want to present myself to others. (#09)
.28
.58
.25
.31
-.04
.57
Self-design
... I want to control what I'm doing with my life. (#10)
.03
.06
.75
.28
.11
.66
.73
... I try to manipulate certain aspects in my life. (#11)
.14
-.05
.73
.02
.14
.57
... I enjoy being my own master. (#12)
.25
.27
.61
.30
-.01
.60
... I'm interested in how certain things in (my) life interact. (#13)
.25
.20
.57
-.14
.03
.44
... it helps me to optimize the way I'm living. (#14)
-.15
.21
.56
.30
.04
.47
Self-
discipline
... it motivates me to keep on working for a goal. (#15)
-.03
.21
.14
.78
.05
.68
.68
... It allows me to reward myself. (#16)
.36
.10
.04
.69
.16
.64
... it facilitates my self-discipline. (#17)
-.10
.07
.37
.67
.11
.62
Self-
heal-
ing
... I don't trust in the healthcare system/classic therapies. (#18)
.06
.11
.08
.12
.86
.78
.80
... I want to be independent from traditional medical treatments. (#19)
.04
.07
.15
.12
.85
.77
Eigenvalue
2.91
2.62
2.52
2.19
1.82
Share of variance explained
.15
.14
.13
.12
.10
Cumulative share of variance explained
.15
.29
.42
.54
.63
Factor 3: Self-design motivated by the possibilities of self-optimization
A total of five items load into this factor. Self-design applies to the self-optimization dimension by self-
tracking. No matter whether it is a self-tracker’s health, fitness, or mood, generally self-trackers are fasci-
nated by the idea of controlling the way they are living by taking responsibility and optimizing their own
lives. At the same time, self-trackers that are strongly motivated by the prospects of self-design are driven
by a need to feel special towards other people (Ludford et al., 2004). Future patients might be seen as
interested in body and brain tuning and willed to optimize their performance self-reliantly, for example by
understanding how certain things in life interact.
Healthcare Information Systems
10 Thirty Fourth International Conference on Information Systems, Milan 2013
Quotes exemplifying this motivation are I wasn't satisfied with my status quo. I wanted more from my
body and my brain (Participant #251) and […] via self-tracking I can identify factors and traits that
mostly affect my everyday behavior and mental/cognitive/physical state, so I could optimize my life by
controlling and manipulating those factors”, (Participant #109).
Factor 4: Self-discipline motivated due to the self-gratification possibilities of self-tracking
In contrast to Self-design, Self-discipline refers to the rewarding and promising aspects of self-tracking,
which might be the prospect of attaining a goal, obtaining a reward, or avoiding a penalty or a negative
consequence (Levesque et al., 2010). Three items load on this component: The facilitation of self-
discipline, motivation to keep on working for a goal, and potential to reward oneself. According to Preece
and Shneiderman (2009), self-disciplining by goal-orientation and a certain need to achieve may increase
the possibility of becoming an actual contributor to a community. People that are motivated by self-
disciplining through self-tracking may [] find it enjoyable to work hard, to be compared to a standard
and to be challenged” (Tedjamulia et al., 2005). Future patients might be willed to collaborate rather
when they experience themselves that self-tracking helps them disciplining, rewarding and motivating
themselves.
Participant #211 wrote that self-tracking helps to motivate me to reach for and achieve certain goals.
Factor 5: Self-healing motivated by the self-healing possibilities of self-tracking
The search of individual therapy alternatives as well as a certain rebellion against the healthcare system is
part of this factor. Self-trackers that are motivated by self-healing “… don’t trust in the healthcare system”
and “… want to be independent from traditional medical treatments”. According to Wolf (2010) human
beings want to understand more and more how they are different from others and how different therapy
alternatives may apply to them. This factor thus represents an increased health-awareness that leads to a
greater need of understanding one’s individual standing in a community and willingness to invest in one’s
health consciously and demandingly. Future generations of HIS may focus on creating and adding value
for the patients themselves by the provision with new information about themselves.
Quotes exemplifying this motivation are I want to know if any number of symptoms are related to each
other, as well as to record facts that I can use to communicate to health professionals or remind myself
of activities from year to year”, (Participant #207) or “Essentially, doctors and personal fitness coaches
have been unable to help. I suffered from insomnia, and found through self-tracking that blackout cur-
tains helped best” (Participant #126).
Construct Validity Assessment
The rigorous scale development methodology with its exploratory principal component analysis and con-
firmatory factor analysis suggest that the five-factor framework of self-tracking motivations possesses
content validity and internal consistency reliability (cf. Section on Research Methodology and Procedure).
This section presents further tests of criterion-related and discriminant validity.
Motivation and intensity of tracking
Intuitively, one should expect that more motivation leads to more activity. This holds true for each of the
five motivational factors individually and assuming an underlying additive structure of motivation for
all five motivational factors jointly. We tested this by investigating the relation of motivation and the in-
tensity of tracking.
Intensity is operationalized by two factors elicited in the survey: The number of parameters a respondent
tracks and the time he or she spends on self-tracking. The number of parameters ranges from 1 to 39
(mean 9, median 8). If, e.g., a respondent stated he would be tracking his steps, weight, and symptoms of
chronic diseases, this is coded as 3 distinct parameters. Responses were elicited by suggesting a list of 45
parameters frequently tracked by self-trackers. Parameters were grouped in nine groups (physical activi-
ties, body, nutrition, well-being, addictions, medical, environment, relationships, other), respondents
could check multiple parameters and each parameter group featured an “other” option with a free text
input. Time spend on self-tracking was elicited with free text fields asking for the number of hours and
Gimpel et al. / Quantifying the Quantified Self
Thirty Fourth International Conference on Information Systems, Milan 2013 11
minutes per day spend on self-tracking. The average time per day is 95 minutes (median 30 minutes). As
data is highly non-normal, a log transformation is applied to the time spend on tracking.
The hypothesis is that motivation increases time spent on tracking. This effect might (partially) be medi-
ated by the number of parameters tracked. Causal mediation analysis investigates this relationship (Baron
and Kenny, 1986; Hayes, 2009). Figure 1 sketches the research model and summarizes the results: Moti-
vations do indeed have a significant effect on activity. Higher scores on any of the motivational factors
significantly increases the number of parameters tracked. When regressing the log of time spent on self-
tracking on the motivations, higher scores on any of the motivational factors significantly increases the
time spent on tracking (data not shown). This effect is partially mediated by the number of parameters
tracked.
In the mediation model, only self-association retains a significant direct effect on time spent beyond the
mediation effect via number of parameters tracked. The interpretation appears straight forward: Self-
association has a community element which requires time to present oneself, to compare oneself to oth-
ers, and to digest feedback from the community.
Figure 1. Causal mediation analysis to assess criterion-related validity of the Five-Factor Frame-
work of Self-Tracking Motivations
Significance codes: ‘***’ .001 ‘**’ .01 ‘*’ .05;
grey, dashed arrows indicate insignificant relationships
Overall, the mediation analysis supports criterion-related validity. The motivational scale does not only
measure reliably, its measurement has predictive power for behavior. In addition, data supports the no-
tion of a cumulative / additive structure of motivation for self-tracking.
Motivation and object of tracking
Self-trackers differ with respect to their motivational profile: some are more driven by Self-entertainment,
some by Self-healing etc. In addition, self-trackers differ in the focus of their tracking: Some focus on
physical activities, others on nutrition, even others focus on other parameters. One should expect a rela-
tionship of these two factors: motivation and object of tracking. We investigate this relationship by a se-
ries of logit regressions.
The parameters tracked by respondents are grouped in nine clusters of parameters. This grouping bases
on pre-tests and was displayed in the survey. For the present analysis, we run a separate logit regression
for each of these nine groups. The dependent variable is a binary dummy indicating whether a respondent
tracks at least one parameter in the group (dummy value 1) or not (dummy value 0). Independent varia-
bles are the five motivational factors. Wald tests are performed for individual coefficients, likelihood ratio
tests for the overall model. Table 3 summarizes the results from regression analysis.
119 out of 150 respondents track at least one parameter on their physical activity; whether an individual
self-tracker does so, is significantly influenced by his or her self-discipline (model 1 in Table 3). The other
Self-entertainment
Self-association
Self-design
Self-discipline
Self-healing
Number of
parameters tracked
Log(time spent
on self-tracking)
Total effect 1.310***
Direct effect: .765**
Mediation effect: .545***
Motivations Activity
Healthcare Information Systems
12 Thirty Fourth International Conference on Information Systems, Milan 2013
four motivational factors have no significant influence on tracking physical parameters. Thus, if one engi-
neers a system or service that relies on tracking physical activities, one has to appeal to participants’ de-
sire for self-discipline and rewarding themselves. Promoting features of the system or service that refer to
self-entertainment, self-association, self-design, or self-healing will be less effective in convincing partici-
pants to track their physical activity. The same holds true for tracking body parameters (model 2).
A system or service engineer trying to motivate tracking of well-being (model 3), nutrition (model 4) or
medical aspects of daily live (model 5) will be most effective when appealing to a combination of Self-
design and Self-healing. For nutrition, referring to Self-discipline will further increase the likelihood of
tracking the intended parameters. For tracking medication, symptoms, or other medical parameters, re-
ferring to Self-discipline has the opposite effect it significantly decreases the likelihood of tracking the
intended parameters. For well-being, the Self-discipline factor is irrelevant.
Table 3. Regression of the object of tracking on motivational factors
Logit regression coefficients (standard errors)
Significance codes: ‘***’ .001 ‘**’ .01 ‘*’ .05; significant coefficients in bold font
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Physical
activities
Body
Well-being
Nutrition
Medical
Environ-
ment
Addictions
Relation-
ships
Other
Number of respondents
150
150
150
150
150
150
150
150
150
Number of respondents
tracking 1 parameter
119
110
89
67
51
38
31
24
106
Self-entertainment
.327
(.212)
.022
(.187)
.194
(.178)
-.282
(.190)
-.095
(.187)
.646**
(.210)
-.212
(.219)
.128
(.229)
-.335
(.190)
Self-association
.279
(.220)
.356
(.198)
.227
(.175)
.118
(.188)
.085
(.192)
.300
(.199)
.518*
(.218)
.024
(.229)
.251
(.192)
Self-design
.328
(.206)
.136
(.185)
.408*
(.178)
.388*
(.192)
.654**
(.223)
.247
(.209)
.459
(.240)
.308
(.246)
.366
(.191)
Self-discipline
.714***
(.215)
.449*
(.189)
-.143
(.176)
.448*
(.194)
-.389*
(.194)
-.004
(.206)
.254
(.230)
.213
(.243)
.249
(.184)
Self-healing
.457
(.245)
.259
(.204)
.369*
(.181)
.877***
(.214)
.738***
(.204)
.037
(.191)
.290
(.205)
.215
(.219)
-.226
(.190)
Intercept
1.595***
(.244)
1.099***
(.199)
.410*
(.175)
-.264
(.184)
-.818***
(.199)
-1.211***
(.209)
-1.525***
(.234)
-1.724***
(.236)
.956***
(.192)
Nagelkerke’s R2
.197**
.103
.114*
.255***
.234***
.131*
.134*
.042
.108*
Interestingly, Self-entertainment only affects tracking of one’s environment (model 6) and Self-
association the tracking of addictions (model 7). For the latter, a causal relationship seems unlikely. It
appears to rather be a case of common cause: People who value association with a community or group
might be more prone to consumption of coffee, cigarettes, and alcohol (the main addiction parameters
tracked) and, thus, more likely to track these parameters. For relationships (model 8) and other parame-
ters like to-do lists and finances (model 9), none of the motivational factors has a significant impact. Like-
ly as the number of respondents tracking relationships is too small and other parameters are too diverse.
Overall, relating the object of tracking to motivational factors is interesting for two reasons: Firstly, the
existence of significant effects adds to the evidence of criterion-related validity. Secondly, data provides
guidance how to motivate people for self-tracking given a specific type of tracking one wants to inspire.
Motivation, personality, and demographics
For the validation of novel measures, convergent as well as discriminant validation is required (Campbell
and Fiske, 1959). Discriminant validity tests whether a novel measure is highly related to existing
measures. If, the five motivational factors proposed in this paper would be highly correlated with estab-
lished scales, their value would be marginal, as other scales could be used for the same purpose.
Gimpel et al. / Quantifying the Quantified Self
Thirty Fourth International Conference on Information Systems, Milan 2013 13
Key candidates for testing discriminant validity of the new scale are personality traits and demographics.
The big five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, openness)
were measured with the short scale validated by (Rammstedt and John, 2007). Demographics were elicit-
ed as age in years and gender. Females are coded as unity, males as zero. 7 respondents did not disclose
their gender; they are excluded for correlating gender with motivation. Table 4 shows the results.
7
Correlation of motivational factors with either common personality traits or demographic characteristics
is generally low and ranges from -.244 to .209. Most correlations are not significantly different from zero.
Thus, we conjecture that the motivational scale for self-tracking has discriminant validity.
Table 4. Correlation of motivational factors and personality traits
Pearson’s product moment correlation, n = 150 (For gender: point-biserial correlation, n = 143)
Significance codes: ‘***’ .001 ‘**’ .01 ‘*’ .05; significant coefficients in bold font
Extraversion
Agreeableness
Conscien-
tiousness
Neuroticism
Openness
Age
Gender
(female = 1)
Self-entertainment
-.130
-.134
-.024
-.018
-.025
.086
-.244*
Self-association
.162*
.020
.107
-.161*
.093
-.008
-.155
Self-design
.036
-.167*
-.017
.191*
.209*
.157
-.019
Self-discipline
.023
-.156
.107
-.052
.038
-.123
-.119
Self-healing
-.172*
.112
.068
.063
.116
.117
.105
Conclusion and Future Work
The revolutionary rise of smartphones together with today’s ubiquitously available internet access has
changed the healthcare delivery landscape. All of the new possibilities through technology have opened up
a world that offers new ways to get to know oneself and to gain a profound, fact-based understanding of
collected self-related data. Patients increasingly become empowered, self-dependent actors in the
healthcare service system. They meticulously record numerous parameters that might be of high rele-
vance for their physicians, including aspects of their physical activities, body, well-being, nutrition, medi-
cation, diagnostics, symptoms, environment, addictions, and the like. Since 2007, this manifests in the
Quantified Self community.
In this paper we addressed the research question what the underlying motivations of self-triggered health
monitoring are. In an exploratory survey among 150 self-trackers, we developed a Five-Factor-Framework
of Self-Tracking Motivations and a psychometric scale with 19 items that cover the five factors of motiva-
tion. These factors are (1) Self-entertainment, (2) Self-association, (3) Self-design, (4) Self-discipline, and
(5) Self-healing. Various methods were used to test convergent, discriminant, and criterion-related validi-
ty of the scale. Quality criteria meet all conventional targets. In brief, the scale seems to measure reliably,
it measures something different than other scales, and its measure is meaningful for explaining intensity
and type of self-tracking.
The present study has obvious shortcomings, most prominently a potential sampling bias and the lack of
replication. The sample of respondents might not be representative for the entire population of self-
trackers. This cannot be judged precisely, as there is neither a full list of self-trackers nor a clear overview
on typical characteristics of self-trackers to compare against our sample. In addition, all our quantitative
analysis relies only on a single sample. The replication with an independent second sample will enhance
external validity and generalizability. It is up to future research to replicate the approach with different
pools of respondents to support or refute our results.
7
Correlation among motivational factors is zero by design (orthogonal rotation). Correlation among personality traits should be
about zero and in fact is indistinguishable from zero for most cases (data not shown). Only exception: extraversion correlates posi-
tively with conscientiousness and openness and negatively with neuroticism.
Healthcare Information Systems
14 Thirty Fourth International Conference on Information Systems, Milan 2013
The paper strongly focuses on patient-driven approaches and falls short of fully covering the extant theory
and practice of health information systems centered on healthcare professionals. Legal and ethical dimen-
sions of patient self-tracking have not been addressed. Despite all these obvious shortcomings, we believe
that our exploratory empirical study is a valuable step in structuring and quantifying the motivations of
self-trackers, a field of study that is relatively young and, so far, largely qualitative and anecdotal.
For health information systems, the question emerges what we can learn from self-trackers for the engi-
neering of physician-initiated self-tracking systems like Remote Health Monitoring (RHM) or Ambulatory
Assessment (AA). The results presented above lead to a three part answer to this question: First, we can
conclude that self-tracking exists for a remarkably heterogeneous group of people. Self-trackers respond-
ing to our survey track up to 39 parameters of their daily life, and they include people from various coun-
tries and continents. The phenomenon exists from teenagers to senior citizens with no significant age
effects and hardly any gender effects. Some self-trackers suffer from chronic diseases, others don’t. In
other words, self-tracking is a robust phenomenon. RHM and AA systems and services frequently suffer
from compliance of patients with self-tracking guidelines suggested by health professionals. Learning
from voluntary self-trackers, understanding their positive motivation for self-tracking, communicating it
to people not directly convinced of the necessity, and using self-trackers as role models is the first implica-
tion for engineering RHM and AA systems and services.
Second, the Five-Factor-Framework of Self-Tracking Motivations provides structure. Data shows that
more motivation on a single factor leads to increased tracking activity and, in addition, motivation from
different factors is cumulative. Thus, the factors along with their description and survey items can inform
engineers of HIS. Depending on the object of tracking (i.e. the parameter one wants people to track), dif-
ferent factors are relevant. The design of RHM and AA systems and services should appeal to the respec-
tive motivations to foster patient compliance.
Third, this structure supports marketing in targeting the right patient or customer population, screening
potential users, and to tailor communication of the benefits of RHM and AA solutions. After deployment
of a solution, the Five-Factor-Framework can support tracking adoption. Apart from the particular HIS-
perspective, the framework can also support healthcare delivery in general. If used by healthcare service
providers like physicians and therapists, they might incite the patients to improve secondary prevention
or monitoring data quality.
In summary, the implication for healthcare information systems is that engineers and developers can
learn how to motivate people to track themselves, how to design systems that appeal to intrinsic motiva-
tors, and how to plan and control implementation success. However, all of these aspects warrant further
research and proof in practical applications.
References
Agarwal, R., Gao, G., DesRoches, C., and Jha, A. K. 2010. Research Commentary - The Digital
Transformation of Healthcare: Current Status and the Road Ahead, Information Systems
Research (21:4), pp. 796-809.
Baron, R. M., and Kenny, D. A. 1986. The Moderator-Mediator Variable Distinction in Social
Psychological Research: Conceptual, Strategic, and Statistical Considerations, Journal of
Personality and Social Psychology (51:6), pp. 1173-1182.
Bélanger, E., Bartlett, G., Dawes, M., Rodríguez, C., and Hasson-Gidoni, I. 2012. Examining the evidence
of the impact of health information technology in primary care: An argument for participatory
research with health professionals and patients, International Journal of Medical Informatics
(81:10) 10, pp. 654-661.
Black, A. D., Car, J., Pagliari, C., Anandan, C., Cresswell, K., Bokun, T., McKinstry, B., Procter, R., Majeed,
A., and Sheikh, A. 2011. The impact of eHealth on the quality and safety of health care: a
systematic overview, PLoS medicine (8:1), e1000387.
Browne, M. W., Cudeck, R., Bollen, K. A., and Long, J. S. 1993. Alternative Ways of Assessing Model Fit,
Sage Focus Editions (154), pp. 136-136.
Butterfield, A. D. 2012. Ethnographic Assessment of Quantified Self Meetup Groups,
http://quantifiedself.com/wp-content/uploads/2012/05/Adam-Project-Report-5-17.pdf,
retrieved May 1, 2013.
Gimpel et al. / Quantifying the Quantified Self
Thirty Fourth International Conference on Information Systems, Milan 2013 15
Byrne, B. M. 1989. A Primer of LISREL: Basic Applications and Programming for Confirmatory Factor
Analytic Models, New York: Springer.
Campbell, D. T., and Fiske, D. W. 1959. Convergent and Discriminant Validation by the Multitrait-
Multimethod Matrix, Psychological Bulletin (56:2), pp. 81-105.
Chaudhry, S. I., Phillips, C. O., Stewart, S. S., Riegel, B., Mattera, J. A., Jerant, A. F., and Krumholz, H. M.
2007. Telemonitoring for Patients With Chronic Heart Failure: A Systematic Review, Journal of
Cardiac Failure (13:1) 2, pp. 56-62.
Csikszentmihalyi, M. 1997. Finding Flow: The Psychology of Engagement with Everyday Life, New York:
Basic Books.
Csikszentmihalyi, M., and Csikszentmihalyi, I. 1975. Beyond Boredom and Anxiety: The Experience of
Play in Work and Games, San Francisco: Jossey-Bass.
Ebner-Priemer, U. W., and Kubiak, T. 2007. Psychological and Psychophysiological Ambulatory
Monitoring, European Journal of Psychological Assessment (23:4), pp. 214-226.
Eysenbach, G. 2008. Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and
Openness, Journal of Medical Internet Research (10:3).
Fahrenberg, J., Myrtek, M., Pawlik, K., and Perrez, M. 2007. Ambulatory Assessment-Monitoring
Behavior in Daily Life Settings, European Journal of Psychological Assessment (23:4), pp. 206-
213.
Fichman, R. G., Kohli, R., and Krishnan, R. 2011. The Role of Information Systems in Healthcare:
Current Research and Future Trends, Information Systems Research (22:3), pp. 419-428.
Fox, S., and Duggan, M. 2013. Tracking for Health, http://www.pewinternet.org/Reports/2013/
Tracking-for-Health.aspx, retrieved May 1, 2013.
Free, C., Phillips, G., Felix, L., Galli, L., Patel, V., and Edwards, P. 2010. The Effectiveness of M-health
Technologies for Improving Health and Health Services: A Systematic Review Protocol, BMC
Research Notes (3:1), p. 250.
Guadagnoli, E., and Velicer, W. F. 1988. Relation of Sample Size to the Stability of Component Patterns,
Psychological Bulletin (103:2), pp. 265-275.
Haux, R. 2006. Health Information Systems? Past, Present, Future, International Journal of Medical
Informatics (75:3-4), pp. 268-281.
Hayes, A. F. 2009. Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium,
Communication Monographs (76:4), pp. 408-420.
Hayton, J. C., Allen, D. G., and Scarpello, V. 2004. Factor retention decisions in exploratory factor
analysis: A tutorial on parallel analysis, Organizational research methods (7:2), pp. 191-205.
Hinkin, T. R. 1998. A Brief Tutorial on the Development of Measures for Use in Survey Questionnaires,
Organizational Research Methods (1:1), pp. 104-121.
Horn, J. L. 1965. A Rationale and Test for the Number of Factors in Factor Analysis, Psychometrika
(30:2), pp. 179-185.
Istepanaian, R. S., and Zhang, Y.-T. 2012. Guest Editorial Introduction to the Special Section: 4G Health
- The Long-Term Evolution of M-health, IEEE Transactions on Information Technology in
Biomedicine (16:1), pp. 1-5.
Jaana, M., and Paré, G. 2007. Home telemonitoring of patients with diabetes: a systematic assessment of
observed effects, Journal of Evaluation in Clinical Practice (13:2), pp. 242-253.
Jaana, M., Pare, G., and Sicotte, C. 2009. "Home telemonitoring for respiratory conditions: a systematic
review, American Journal of Managed Care (15:5), pp. 313-320.
Kelley, H., Chiasson, M., Downey, A., and Pacaud, D. 2011. The Clinical Impact of eHealth on the Self-
Management of Diabetes: a Double Adoption Perspective, Journal of the Association for
Information Systems (12:3), pp. 208-234.
Kelly, K., Wolf, G., Carmichael, A., and Ramirez, E. 2007. Quantified Self About,
http://quantifiedself.com/about/, retrieved May 1, 2013.
Levesque, C., Copeland, K. J., Pattie, M. D., and Deci, E. L. 2010. Intrinsic and Extrinsic Motivation, in
Peterson, P., Baker, E., McGaw, B. (eds.), International Encyclopedia of Education (Third
Edition), Oxford: Elsevier, pp. 618-623.
Long, J. S. 1983. Confirmatory factor analysis: A preface to LISREL, Newburry Park:SAGE Publications.
Ludford, P. J., Cosley, D., Frankowski, D., and Terveen, L. 2004. Think Different: Increasing Online
Community Participation Using Uniqueness and Group Dissimilarity, Proceedings of the
SIGCHI conference on Human Factors in Computing Systems, ACM2004, pp. 631-638.
Healthcare Information Systems
16 Thirty Fourth International Conference on Information Systems, Milan 2013
Mandl, K. D., and Kohane, I. S. 2008. Tectonic Shifts in the Health Information Economy, New
England Journal of Medicine (358:16), pp. 1732-1737.
Mehl, M. R., and Holleran, S. E. 2007. An Empirical Analysis of the Obtrusiveness of and Participants'
Compliance with the Electronically Activated Recorder (EAR), European Journal of
Psychological Assessment (23:4), pp. 248-257.
Nunnally, J. C. 1978. Psychometric Theory, New York: McGraw-Hill.
Oppenheimer, D. M., Meyvis, T., and Davidenko, N. 2009. Instructional manipulation checks: Detecting
satisficing to increase statistical power, Journal of Experimental Social Psychology (45:4), pp.
867-872.
Paré, G., Jaana, M., and Sicotte, C. 2007. Systematic Review of Home Telemonitoring for Chronic
Diseases: The Evidence Base, Journal of the American Medical Informatics Association (14:3),
pp. 269-277.
Polisena, J., Tran, K., Cimon, K., Hutton, B., McGill, S., Palmer, K., and Scott, R. E. 2010. Home
telehealth for chronic obstructive pulmonary disease: a systematic review and meta-analysis,
Journal of Telemedicine and Telecare (16:3), pp. 120-127.
Preece, J., and Shneiderman, B. 2009. The Reader-to-Leader Framework: Motivating Technology-
Mediated Social Participation, AIS Transactions on Human-Computer Interaction (1:1), pp. 13-
32.
Rammstedt, B., and John, O. P. 2007. Measuring personality in one minute or less: A10-item short
version of the Big Five Inventory, Journal of Research in Personality, pp. 203-212.
Robinson, J. 1991. Criteria for scale selection and evaluation, in Robinson, J.P., Shaver, P.R.,
Wrightsman, L.S. (eds), Measures of Personality and Social Psychological Attitudes, San
Diego:Academic Press.
Schmidt, S., Schuchert, A., Krieg, T., and Oeff, M. 2010. Home Telemonitoring in Patients with Chronic
Heart Failure: A Chance to Improve Patient Care? Deutsches Ärzteblatt International (107:8), p.
131.
Schriesheim, C. A., Powers, K. J., Scandura, T. A., Gardiner, C. C., and Lankau, M. J. 1993. Improving
Construct Measurement in Management Research: Comments and a Quantitative Approach for
Assessing the Theoretical Content Adequacy of Paper-and-Pencil Survey-Type Instruments,
Journal of Management (19:2), pp. 385-417.
Shin, M. 2012. Secure Remote Health Monitoring with Unreliable Mobile Devices, Journal of
Biomedicine and Biotechnology, Article ID 546021.
Singh, R., Mathiassen, L., Stachura, M. E., and Astapova, E. V. 2011. Dynamic Capabilities in Home
Health: IT-Enabled Transformation of Post-Acute Care, Journal of the Association for
Information Systems (12:2), pp. 163-188.
Stone, E. F., and Stone Eugene, F. 1978. Research Methods in Organizational Behavior, Santa Monica:
Goodyear Publishing Company .
Swan, M. 2009. Emerging Patient-Driven Health Care Models: An Examination of Health Social
Networks, Consumer Personalized Medicine and Quantified Self-Tracking, International
Journal of Environmental Research and Public Health (6), pp. 492-525.
Tedjamulia, S. J., Dean, D. L., Olsen, D. R., and Albrecht, C. C. 2005. Motivating Content Contributions
to Online Communities: Toward a more Comprehensive Theory, HICSS '05 Proceedings of the
38th Annual Hawaii International Conference on System Sciences, pp. 193.2.
TheEconomist. 2012. The Quantified Self: Counting Every Moment, http://www.economist.com/
node/21548493, retrieved May 1, 2013.
Trueman, P. 2009. Economic Considerations of Remote Monitoring in Chronic Conditions, Eurohealth
(15:1), p. 10.
Trull, T. J., and Ebner-Priemer, U. 2012. Ambulatory Assessment, Annual Review of Clinical
Psychology (9), pp. 151-176.
Wolf, G. 2008. Quantified Self, http://aether.com/quantifiedself, retrieved May 1, 2013.
Wolf, G. 2010. The Data-Driven Life, http://www.nytimes.com/2010/05/02/magazine/
02self-measurement-t.html, retrieved May 1, 2013.
... Looking at research regarding data disclosure among peers and/or on public platforms (within an app for example), cost-benefit trade-offs in this context are strongly linked to situationally perceived suffering and experience [59]. The prospects of fulfilling a feeling of belonging (to a community) and identification with the personalized and individual data can outweigh possible negatives, for example, receiving personalized advertisements or privacy concerns [60,61]. Lupton [51] states, that online patient support groups such as PatientsLikeMe as well as Facebook groups and other social media also encourage members to disclose their health, fitness and medical details as a way of contributing to peer networks of expertise and support. ...
... Since situational perceived pressure of suffering can outweigh privacy concerns, individuals who aim to reduce their personal suffering are thus more likely to donate sensitive health data for research in return for the prospect of a better therapy option in the future. Indirect reciprocity can explain this behavior: giving back (to the community), expecting the same treatment in return [59][60][61]. This could imply that apps specifically for disease management offer a promising first gateway for implementing data donation requests. ...
Article
Full-text available
Health self-tracking is an ongoing trend as software and hardware evolve, making the collection of personal data not only fun for users but also increasingly interesting for public health research. In a quantitative approach we studied German health self-trackers (N = 919) for differences in their data disclosure behavior by comparing data showing and sharing behavior among peers and their willingness to donate data to research. In addition, we examined user characteristics that may positively influence willingness to make the self-tracked data available to research and propose a framework for structuring research related to self-measurement. Results show that users’ willingness to disclose data as a “donation” more than doubled compared to their “sharing” behavior (willingness to donate = 4.5/10; sharing frequency = 2.09/10). Younger men (up to 34 years), who record their vital signs daily, are less concerned about privacy, regularly donate money, and share their data with third parties because they want to receive feedback, are most likely to donate data to research and are thus a promising target audience for health data donation appeals. The paper adds to qualitative accounts of self-tracking but also engages with discussions around data sharing and privacy.
... Previous and guidelines from various international hypertension associations were reviewed to provide a basis for development of the scale (19)(20)(21). Patients with hypertension above 18 years old, had no communication barrier and agreed to participate in the study voluntarily, and signed the informed consent form were interviewed. Data saturation was used as the termination index in the interview. ...
... The instrument had seven domains including external reward, internal reward, severity, susceptibility, response efficacy, selfefficacy, and response cost. The group proposed some possible items under each of the facets within each domain, resulting in a pool of 43 items, after reviewing self-quantification motivations instruments (21) and considering the elements of Chinese culture. A convenience sample of ten patients with hypertension was used to obtain feedback on the language and clarity of the questionnaire. ...
Article
Full-text available
Background The self-management ability of patients with hypertension is poor, and self-quantification increases gradually with the development of electronics. Self-quantification for patients with hypertension has important implications for individual health. However, there is a lack of relevant scales at present, and we aim to develop a self-quantified scale for patients with hypertension. Methods The instrument was developed based on protection motivation theory with literature review, a qualitative interview study and focus group discussions, and pilot testing. A total of 360 patients with hypertension were investigated using the scale. The psychometric properties of the scale were evaluated concerning validity and reliability employing internal consistency reliability, split-half reliability, test-retest reliability, content validity (S-CVI/Ave and I-CVI), and construct validity (exploratory factor analysis and confirmatory factor analysis). Results The final scale had 30 items with seven sub-domains. The Cronbach's α for all domains was 0.900 with a range of 0.817–0.938. The split-half reliability coefficient for all domains was 0.743 with a range of 0.700–0.888. The test-retest reliability coefficient for all domains was 0.880 with a range of 0.849–0.943. The S-CVI/Ave for all domains was 0.922 with a range of 0.906- 0.950, and the I-CVI of each item was a range of 0.800–1.000. The result of confirmatory factor analysis of this scale showed that χ2/df was 2.499, RMSEA = 0.065, GFI=0.865, NFI=0.894, IFI=0.934, TLI=0.914, CFI=0.933, RFI=0.865. The Pearson's coefficients between the total scale and every domain were ranging from 0.347 to 0.695, and each domain ranged from 0.130 to 0.481. Conclusion The scale has good validity and reliability and can be used as a self-quantification scale for patients with hypertension.
... 2.6.2. Moderating Effect of Self-Discipline Motivation "Self-discipline motivation" refers to the perceived ability and determination of the users to maintain self-discipline and use healthcare information from mHealth services [53]. Users of mHealth apps with health-related goals decide to model the articulated information and monitor their health goals to be rewarded or to avoid punishment. ...
... To measure attitude towards mHealth apps, four items adapted from Hossain, Ang, Chng, and Wong [39] were used including "I am keen to learn about and try new mobile health solutions in future". Self-discipline motivation was assessed using a three-item scale from Gimpel, Nißen, and Görlitz [53] and included items such as "I am allowed to reward myself each time I make a small progress in achieving my health goals". To measure the behavioral intention of using mHealth, the present study adopted four items from Cho et al. [56] such as "I intend to use mHealth in the next few days". ...
Article
Full-text available
Recent advancements in mHealth apps and services have played a vital role in strengthening healthcare services and enabling their accessibility to marginalized people. With the alarming rise in COVID-19 infection rates around the world, there appears to be an urgent call to modernize traditional medical practices to combat the pandemic. This study aims to investigate the key factors influencing the trialability of mHealth apps/services and behavioral intention to adopt mobile health applications. The study also examines the moderating effects of self-discipline motivation, knowledge, and attitude on the relationship between trialability and behavioral intention to use. The deductive reasoning approach was followed in a positivism paradigm. The study used convenience sampling and collected responses from 280 Generation Y participants in Bangladesh. Partial least square-based structural equation modeling was employed. The results revealed that relative advantage (β = 0.229, p < 0.05), compatibility (β = 0.232, p < 0.05), complexity (β = −0.411, p < 0.05), and observability (β = 0.235, p < 0.05) of mHealth apps influence the trialability of mHealth apps and services among users. Trialability compatibility (β = 0.425, p < 0.05) of mHealth was positively related to the behavioral intention to use these mobile apps. The study found no moderating effects of attitude (β = 0.043, p > 0.05) or self-discipline motivation (β = −0.007, p > 0.05) on the hypothesized relationships. The empirical findings of this study may facilitate the development, design process, and implementation of mHealth applications with improved features that can lead to high user acceptance among Generation Y during future health crises.
... Some questioned if this technology would take away one's intelligence, like intuitive knowledge about one's health, which relates to concerns about the erosion of human agency and autonomy by over-relying on digital tools [28,30], as AI systems tend to increasingly act independently and take over control [59]. However, it might give individuals more freedom in making decisions or even give the feeling of self-control, if in people self-design and self-optimize how their DT looks, behaves, and collects and uses their data [60], considering there is still a human-in-the-loop to discuss and support decision making. For example, people can decide when to go to a doctor, what to share with professionals, and how to look for certain treatments. ...
Chapter
Full-text available
Digital twins refer to a digital replica of potential actual physical assets, people, or systems, which are relevant for the future of digital health. These virtual replicas can be used to perform simulations that help assess possible risks, test performance, and optimize processes before applying them in the real world. Applied to the healthcare sector, a digital twin can refer to a replica of a patient or certain aspects of a human, like body parts, body organs or body systems. As a digital twin would age with the owner the question arises as to how we should visualize our digital twin (i.e., how to represent ourselves in a digital way with data). We do not yet know how people want their data (quantitative or qualitative) to be represented as digital twins. We addressed this question using generative design research methods, and more particularly co-design sessions that explored users’ perspectives and design preferences on digital twins. Our findings suggest a preference for qualitative representation unless there are emergency alerts, in which quantitative representations were preferred. People were reluctant towards health forecasting through a digital twin and saw it more as a reflection tool to improve quality of life.KeywordsDigital twinsVisualizationQualitative displays/interfacesAffective designAffective atmospheres
... With self-tracking devices, one can track a wide range of data relating to one's bodily functions and everyday habits from steps walked, heart rate, body fat to pain levels. Most of the users who perform personal tracking activity monitor physical activity (exercise, step count, etc.), body characteristics (weight, heart rhythm, etc.), well-being (sleep cycles and quality, stress management, etc.), nutrition and medical health (Appelboom et al. 2014;Gimpel et al. 2013;Rooksby et al. 2014;Swan 2009). Selftracking is also popular for symptom tracking (zone or frequency of illness), treatment tracking or following biodata in order to prevent illness. ...
Article
Full-text available
This study investigates the effects of information supplied by self-tracking technologies on the human-technology relationship through a post-phenomenological approach. Self-tracking technologies, which have become increasingly popular among users since 2000, nowadays, provide biodata to individuals in many different areas from daily step count to heart rhythm or from sleep quality to symptom tracking. The first part of the paper revisits post-phenomenological approach that is a relatively new approach analyzing the human-technology relation. The empirical focus of the study is grounded on the motivation for applying self-tracking gadgets, perceptions of gathered data, potential changes in the conception of the self-knowledge through mediated data and its possible consequences. For the empirical research an open-text survey is conducted with 26 people who were users of a self-tracking device. The findings suggest that self-tracking activity through wearable technology affects the perception of self-knowledge and preliminary results also indicate a dependency to measured data more than it is needed. The results contribute to a more nuanced understanding of adoption of the emerging wearable technology in daily life.
... In particular, de Maeyer and Markopoulos (2020) especially highlight the possibility of self-design and better self-discipline in HDT-based therapy. Here, self-design describes how a HDT could support motivation by tracking and visualising the patient's progress while self-discipline refers to motivation resulting from increased goal orientation through reminders and reward according to the patient's individual habits and desires (Gimpel et al., 2013). Such feedback could for instance be directly visualised on an embodiment of the HDT while being supported by audio or haptic channels, e.g. the control of an exoskeleton supporting the patient during therapy, all coordinated by the HDT considering both information about the specific user (e.g. the patient or therapist) and the given context (e.g. ...
Article
Full-text available
One of the most promising trends in healthcare digitalisation is the personalisation and individualisation of therapy based on virtual representations of the human body through Human Digital Twins (HDTs). Despite the growing number of articles on HDTs, to-date no consensus on how to design such systems exists. A systematic literature review for designing HDTs used for behaviour-changing therapy and rehabilitation resulted in eight key design considerations across four themes: regulatory and ethical, transparency and trust, dynamism and flexibility, and behaviour and cognitive mechanisms.
... It should be analyzed whether there are differences in personality, behavioral patterns, or other characteristics in comparison to non-users. Future studies should as well build on works like that of Gimpel et al. (2013) to determine which motivations lead users to engage in self-tracking. Similarly, it is yet unclear whether there are users who benefit more or less from the provision of autonomy affordance. ...
Thesis
Full-text available
Digitalization has long since entered and transformed our professional lives, our interaction with companies, and our private lives. With the progress in digitalization in general and of individuals in particular, both opportunities and challenges arise. Digitalization represents a double-edged sword, with its vast potential on the one end and a number of risks and detrimental effects for individuals, such as technostress, on the other. Individuals need to navigate the opportunities provided by digitalization, as well as its risks, in all areas of their lives. Addressing digitalization in a way that is in the best interest of individuals requires a thorough understanding of developments, challenges, and possible interventions and solutions. Matt et al. (2019) propose a framework for studying the digitalization of individuals, which represents a holistic approach to structure, classify, and position research along different roles of individuals from a comprehensive set of research angles. By applying this framework as a guiding structure, this dissertation aims to advance knowledge for an improved, safer, and more deliberate navigation of digitalization for individuals in their roles as employees, customers, and themselves from the research angles design, behavior, and consequences. While building on and integrating qualitative research methods such as literature analysis and expert interviews, this dissertation mainly relies on the collection of empirical data and their quantitative analysis. This comprises several small- and large-scale surveys and field experiments, as well as analytical methods such as structural equation modeling, regression analysis, and cluster analysis. Chapter 2 of this dissertation discusses the digitalization of individuals in their role as employees. Chapter 2.1 covers workplace design in terms of equipment with digital workplace technologies (DWTs) and the user behavior of employees. It determines which DWTs exist and are used by individual employees in a comprehensive and structured fashion. Contributing to a deeper understanding of workplace digitalization, chapter 2.1 also demonstrates and elaborates how this overview of DWTs represents a basis for individualized digital work design as well as adequate interventions. Chapter 2.2 deals with the consequences of DWT user behavior. It focuses on the relationship between workplace digitalization, the negative consequence technostress, and possible countermeasures termed “technostress inhibitors.” By enabling a more detailed understanding of the underlying mechanisms as well as evaluating the effects of countermeasures, chapter 2.2 discusses the overall finding that workplace digitalization increases technostress. The dynamics of its different components and technostress inhibitors, however, require individual consideration at a more detailed level, as the interrelationships are not consistently intuitive. In chapter 3, the focus changes to individuals in their role as customers. As a response to increasing data collection by companies as well as increasing data privacy concerns of customers, chapter 3.1 focuses on the identification of a comprehensive list of data privacy measures that address these concerns. Furthermore, it is identified that the implementation of some of these measures would lead to increased customer satisfaction, demonstrating that there is an upside to data privacy for companies and that mutually beneficial outcomes for both involved parties are conceivable. Chapter 3.2 analyzes whether and how digital nudging can be applied to influence customers’ online shopping behavior towards the selection of more environmentally sustainable products in online supermarkets and how this influence differs with respect to individual customer characteristics. It determines the digital nudging element “default rules” to be generally effective and “simplification” to be effective among environmentally conscious customers. On a macro level, the findings contribute to a safer environment in which individuals live their lives, while at the individual level, they foster decision-making quality and health. Chapter 4 highlights the digitalization of individuals themselves. Chapter 4.1 deals with the design of a habit-tracking app that offers users autonomy in their goal-directed behavior. It is found that the provision of autonomy enhances well-being. Its exercise improves performance, which in turn positively affects well-being. Chapter 4.1 thus contributes insights into how digital technologies can foster the flourishing of users. As a summary, this dissertation aims to provide research and practice with contributions to a deeper understanding of how individuals as employees, customers, and themselves can successfully navigate digitalization.
... Cluster 1c covers the research focus of HIS use and maintenance, as well as the consequences of HIS. Whereas most papers addressing the HIS acceptance theme focus on professionals' or patients' acceptance of specific technological solutions, such as telemedicine (Djamsbi et al., 2009) or electronic health records (Gabel et al., 2019), papers assigned to health information interchange focus on topics related to information disclosure, such as selftracking applications (Gimpel et al., 2013). Finally, the HIS outsourcing and performance theme concentrates on financial aspects in organizations, including potential for quality improvements and cost reductions (Setia et al., 2011;Singh et al., 2011). ...
Article
Full-text available
Accelerated by the coronavirus disease 2019 (Covid-19) pandemic, major and lasting changes are occuring in healthcare structures, impacting people's experiences and value creation in all aspects of their lives. Information systems (IS) research can support analysing and anticipating resulting effects. The purpose of this study is to examine in what areas health information systems (HIS) researchers can assess changes in healthcare structures and, thus, be prepared to shape future developments. A hermeneutic framework is applied to conduct a literature review and to identify the contributions that IS research makes in analysing and advancing the healthcare industry. We draw an complexity theory by borrowing the concept of 'zooming in and out', which provides us with a overview of the current, broad body of research in the HIS field. As a result of analysing almost 500 papers, we discovered various shortcomings of current HIS research. We derive future pathways and develop a research agenda that realigns IS research with the transformation of the healthcare industry already under way.
Article
Many internet news sites have introduced recommendation systems to help users mitigate information overload. However, these systems may exacerbate mindless information consumption by reducing opportunities for people to voluntarily select news. We propose prototypes of personal informatics tools based on a self-regulatory process and quantified self-theories, which can be used to help people fulfill a eudaimonic motivation through self-observation and self-control and thus lead to deliberate news consumption. A longitudinal field experiment demonstrates that self-control and self-observation tools promote deliberate news consumption and reveals synergistic effects between the two tools. Our results indicate that the effect of the self-observation tool persists longer.
Article
Full-text available
In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators. (46 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Article
Full-text available
Information systems have great potential to reduce healthcare costs and improve outcomes. The purpose of Ithis special issue is to offer a forum for theory-driven research that explores the role of IS in the delivery of healthcare in its diverse organizational and regulatory settings. We identify six theoretically distinctive elements of the healthcare context and discuss how these elements increase the motivation for, and the salience of, the research results reported in the nine papers comprising this special issue. We also provide recommendations for future IS research focusing on the implications of technology-driven advances in three areas: social media, evidence-based medicine, and personalized medicine.
Article
Full-text available
Ambulatory assessment refers to the use of computer-assisted methodology for self-reports, behavior records, or physiological measurements, while the participant undergoes normal daily activities. Since the 1980s, portable microcomputer systems and physiolog- ical recorders/analyzers have been developed for this purpose. In contrast to their use in medicine, these new methods have hardly entered the domain of psychology. Questionnaire methods are still preferred, in spite of the known deficiencies of retrospective self-reports. Assessment strategies include: continuous monitoring, monitoring with time- and event-sampling methods, in-field psychological testing, field experimentation, interactive assessment, symptom monitoring, and self-management. These approaches are innovative and address ecological validity, context specificity, and are suitable for practical applications. The advantages of this methodology, as well as issues of acceptance, compliance, and reactivity are discussed. Many technical developments and research contributions have come from the German-speaking countries and the Netherlands. Nonetheless, the current Decade of Behavior (APA) calls for a more widespread use of such techniques and developments,in assessment. This position paper seeks to make,the case for this approach,by demonstrating,the advantages – and in some domains,– necessities of ambulatory,monitoring methodology for a behavioral science orientation in psychology. Keywords: ambulatory assessment (monitoring), computer-assisted methods, decade of behavior, ecological validity, electronic diary Assessing human experience and behavior, both in the
Chapter
Intrinsic motivation underlies behaviors performed purely for interest and enjoyment; extrinsic motivation underlies behaviors performed to obtain separable rewards or avoid negative outcomes. Different types of extrinsic motivations exist and can be placed on a self-determination continuum. Intrinsic motivation and self-determined forms of extrinsic motivation facilitate positive outcomes such as well-being. Non-self-determined forms of extrinsic motivation are associated with negative outcomes such as anxiety. Autonomy-supportive environments which provide choices and options foster the development of intrinsic motivation and self-determination. In education, autonomy-supportive environments provide the context for greater learning outcomes such as increased classroom involvement, performance, and satisfaction.
Article
The original idea for this handbook of attitude and personality measures came from Robert Lane, a political scientist at Yale University. Like most social scientists, Lane found it difficult to keep up with the proliferation of social attitude measures. In the summer of 1958, he attempted to pull together a broad range of scales that would be of interest to researchers in the field of political behavior. Subsequently, this work was continued and expanded at the Survey Research Center of the University of Michigan under the general direction of Philip Converse, with support from a grant by the National Institute of Mental Health. The result was a three-volume series, the most popular of which was the last, Measures of Social Psychological Attitudes. That is the focus of our first update of the original volumes. Readers will note several differences between this work and its predecessors. Most important, we have given responsibility for each topic to experienced and well-known researchers in each field rather than choosing and evaluating items by ourselves. These experts were also limited to identifying the 10 or 20 most interesting or promising measures in their area, rather than covering all available instruments. This new structure has resulted in more knowledgeable review essays, but at the expense of less standardized evaluations of individual instruments. There are many reasons for creating a volume such as this. Attitude and personality measures are likely to appear under thousands of book titles, in dozens of social science journals, in seldom circulated dissertations, and in the catalogues of commercial pub-lishers, as well as in undisturbed piles of manuscripts in the offices of social scientists. This is a rather inefficient grapevine for the interested researcher. Too few scholars stay in the same area of study on a continuing basis for several years, so it is difficult to keep up with all of the empirical literature and instruments available. Often, the interdisciplinary investigator is interested in the relation of some new variable, which has come to attention casually, to a favorite area of interest. The job of combing the literature to pick a proper instrument consumes needless hours and often ends in a frustrating decision to forego measuring that characteristic, or worse, it results in a rapid and incomplete attempt to devise a new measure. Our search of ihe literature has revealed unfortunate replications of previous discoveries as well as lack of attention to better research done in a particular area. The search procedure used by our authors included thorough reviews of Psychologi-cal Abstracts as well as the most likely periodical sources of psychological instruments (e.g., Journal of Personality and Social Psychology, Journal of Personality Assessment, Journal of Social Psychology, Personality and Social Psychology Bulletin, Child Devel-opment, and the Journal of Applied Psychology) and sociological and political measures (Social Psychology Quarterly, American Sociological Review, Public Opinion Quarterly, and American Political Science Review). Doctoral dissertations were searched by examin-ing back issues of Dissertation Abstracts. Personal contact with the large variety of empirical research done by colleagues widened the search, as did conversations with researchers at annual meetings of the American Sociological Association and the Ameri-can Psychological Association, among others. Papers presented at these meetings also served to bring a number of new instruments to our attention. Our focus in this volume is on attitude and personality scales (i.e., series of items with homogeneous content), scales that are useful in survey or personality research set-tings as well as in laboratory situations. We have not attempted the larger and perhaps hopeless task of compiling single attitude items, except for ones that have been used in large-scale studies of satisfaction and happiness (see Chapter 3). While these often tap important variables in surveys and experiments, a complete compilation of them (even for happiness) is beyond our means. Although we have attempted to be as thorough as possible in our search, we make no claim that this volume contains every important scale pertaining to our chapter headings. We do feel, however, that our chapter authors have identified most of the high quality instruments.
Article
This article is concerned with measures of fit of a model. Two types of error involved in fitting a model are considered. The first is error of approximation which involves the fit of the model, with optimally chosen but unknown parameter values, to the population covariance matrix. The second is overall error which involves the fit of the model, with parameter values estimated from the sample, to the population covariance matrix. Measures of the two types of error are proposed and point and interval estimates of the measures are suggested. These measures take the number of parameters in the model into account in order to avoid penalizing parsimonious models. Practical difficulties associated with the usual tests of exact fit or a model are discussed and a test of “close fit” of a model is suggested.