Microlearning mApp raises health competence: hybrid service design

Abstract

Work place health support interventions can help support our aging work force, with mApps offering cost-effectiveness opportunities. Previous research shows that health support apps should offer users enough newness and relevance each time they are used. Otherwise the ‘eHealth law of attrition’ applies: 90 % of users are lost prematurely. Our research study builds on this prior research with further investigation on whether a mobile health quiz provides added value for users within a hybrid service mix and whether it promotes long term health? We developed a hybrid health support intervention solution that uses a mix of electronic and physical support services for improving health behaviours, including a mobile micro-learning health quiz. This solution was evaluated in a multiple-case study at three work sites with 86 users. We find that both our mobile health quiz and the overall hybrid solution contributed to improvements in health readiness, −behaviour and -competence. Users indicated that the micro-learning health quiz courses provided new and relevant information. Relatively high utilization rates of the health quiz were observed. Participants indicated that health insights were given that directly influenced every day health perceptions, −choices, coping and goal achievement strategies, plus motivation and self-norms. This points to increased user health self-management competence. Moreover, even after 10 months they indicated to still have improved health awareness, −motivation and -behaviours (food, physical activity, mental recuperation). A design analysis was conducted regarding service mix efficacy; the mobile micro-learning health quiz helped fulfil a set of key requirements that exist for designing ICT-enabled lifestyle interventions, largely in the way it was anticipated.

Full text

ORIGINAL PAPER
Microlearning mApp raises health competence:
hybrid service design
Luuk P. A. Simons &Florian Foerster &Peter A. Bruck &
Luvai Motiwalla &Catholijn M. Jonker
Received: 31 August 2014 /Accepted: 20 January 2015
#IUPESM and Springer-Verlag Berlin Heidelberg 2015. This article is published with open access at Springerlink.com
Abstract Work place health support interventions can help
support our aging work force, with mApps offering cost-
effectiveness opportunities. Previous research shows that
health support apps should offer users enough newness and
relevance each time they are used. Otherwise the ‘eHealth law
of attrition’applies: 90 % of users are lost prematurely. Our
research study builds on this prior research with further inves-
tigation on whether a mobile health quiz provides added value
for users within a hybrid service mix and whether it promotes
long term health? We developed a hybrid health support inter-
vention solution that uses a mix of electronic and physical
support services for improving health behaviours, including
a mobile micro-learning health quiz. This solution was evalu-
ated in a multiple-case study at three work sites with 86 users.
We find that both our mobile health quiz and the overall hy-
brid solution contributed to improvements in health readiness,
−behaviour and -competence. Users indicated that the micro-
learning health quiz courses provided new and relevant infor-
mation. Relatively high utilization rates of the health quiz
were observed. Participants indicated that health insights were
given that directly influenced every day health perceptions,
−choices, coping and goal achievement strategies, plus moti-
vation and self-norms. This points to increased user health
self-management competence. Moreover, even after
10 months they indicated to still have improved health aware-
ness, −motivation and -behaviours (food, physical activity,
mental recuperation). A design analysis was conducted re-
garding service mix efficacy; the mobilemicro-learning health
quiz helped fulfil a set of key requirements that exist for de-
signing ICT-enabled lifestyle interventions, largely in the way
it was anticipated.
Keywords Mobile health .mApp .Service design .
Multi-channel services .Healthy lifestyle intervention .
Self-management .Health competence
1 Introduction
Work place health support interventions are very relevant: to
support our aging work force and to help reduce the private
and social burdens of preventable disease. There are two de-
sign challenges there: increasingly more support is required,
and this has to be delivered at lower costs. Hence, there is an
urgency to develop automated eHealth solutions to improve
user’shealth.
Previous research highlighted the following requirements
in the work context: solutions need to be efficient; they should
limit their intrusion on the other work tasks [3]; they should
offer enough newness and relevance [24] each time the mobile
health application is used. Otherwise the eHealth law of attri-
tion applies: 90 % of users are lost prematurely [7].
L. P. A. Simons (*):C. M. Jonker
Delft University of Technology, Delft, The Netherlands
e-mail: l.p.a.simons@tudelft.nl
C. M. Jonker
e-mail: C.M.Jonker@tudelft.nl
F. Fo erst er
Georgia Institute of Technology, Atlanta, GA, USA
e-mail: florian.foerster@gatech.edu
P. A. Bruck
Research Studios Austria Forschungsgesellschaft mbH,
Wien, Austria
e-mail: bruck@researchstudio.at
L. Motiwalla
Operations & Info Systems Department, University of Massachusetts
Lowell, Lowell, MA, USA
e-mail: luvai_motiwalla@uml.edu
Health Technol.
DOI 10.1007/s12553-015-0095-1

This eHealth law of attrition is an important design chal-
lenge. On the one hand, we know that many health improve-
ment initiatives of people fail and that with extra support,
success rates improve [1,13]. On the other hand, automated
eHealth support is often the economically preferred way (es-
pecially in the future, given our aging population). But if
eHealth applications generally lose 90 % of their users within
several usage instances [7], we are not adding enough value
with eHealth.
In previous research [23] we have shown that various mo-
bile health applications, even when they are popular mApps
from the App stores, still face challenges in delivering added
value in existing health coach relationships. In several of our
real world user tests, the ‘eHealth law of attrition’applied [7].
In other words, a large majority of users (>90 %) stopped
using the mApps within 2 weeks and judged that they added
little new information or value after a few initial usage events.
Very similar patterns were found for a food choices app
(CalorieTeller), physical activity (Runkeeper) and a stress
management app (Pranayama). Two main design lesson con-
clusions were [24,25]: First, part of the attractiveness of an
mApp appears to be the ‘newness factor’: do I learn or expe-
rience something new? Second, it helps when the mApp is a
core element of a larger service mix within an existing health
coach relationship, see also [11].
Moreover, as part of qualitative user feedback, we found a
continuous hunger from health coach participants for new
health information. It was decided to create a health coach
service mix, where the processes of health education would
be supported with a mobile health education system in the
form of a micro-learning Health Quiz on multiple health be-
haviour themes (food, physical activity, mental energy, long
term sustainable health behaviours).
The high-level research questions we ask are: Can a mobile
health-quiz provide added value within a hybrid (electronic
and face to face) health coach service mix? Do mobile
health-quiz app promote long term health behaviour, readiness
(awareness, motivation, plans and actions) and health compe-
tence? To answer these questions we conducted a multiple-
case study in three different work site environments
(employers) and addressed the research question via four spe-
cific questions: First, does the mobile health quiz provide
added value (usefulness, fun, positive triggers) with low bar-
riers (ease of use, limited time burdens)? Second, can the
mobile health quiz be integrated in effective health coach
processes and overall service mix? Third, do the mobile
health quiz and the overall service mix improve health
readiness (awareness, motivation, plans and actions) and
health competence (health perceptions, everyday choices,
coping and goal achievement strategies, growth and
health identity)? Fourth, do the mobile health quiz and
the overall service mix improve health behaviours, short
termandlongterm?
2 Theory
For health coaching solutions to be effective, they need to help
improve health readiness as indicated in the HAPA (Health
Action Process Approach) model [16,14] and i-change
models [6]. Three important phases are distinguished. Barriers
or motivators for change can reside in each of these phases,
which are: awareness, motivation/intention, and practice (in-
cluding planning, experiencing, coping, improving). And as
an underlying theme self-efficacy is important in these
models, namely: can we support people in developing skills
to live more healthily and with tactics to deal with challenges?
In practice, around 90 % of daily perceptions and choices
occur largely automatically, using a set of competences that
Kahneman [12] describes as made by the ‘system 1’fast de-
cision making parts of our brain. This includes perceptions
and choices related to healthy lifestyle. This regards choices
like:
‘Did you come to work by bicycle this morning? What
did you have for breakfast? Did you brush your teeth?’
and ‘How long did you take to pause and think about
these choices?’
If you make healthy choices ‘on auto pilot’they require
little effort (or self-control or will power, [2]). Then they have
a larger chance to become long term health patterns, especially
if reinforced by self-evaluative perceptions of health identity,
preferences and quality of life [12] and patterns of personal
growth [18]. Collectively, these skills and patterns form a
health competence set. Interestingly, the higher people’s
health competence levels, the higher their happiness levels
appear to be [10]. In summary, this health competence set
consists of health perceptions, everyday choices, coping and
goal achievement strategies, growth and health identity. Like
any competence set, these are open to training and develop-
ment [18], which is the aim of our health coach intervention.
If we look at the design challenge of persuasive technology
[8,9] for health, it was theorized and tested elsewhere that this
challenge is not just located in the ICT design, but also in the
design of the overall service scape, including health effects
and coach relationship [25]. It should generate positive, mu-
tually reinforcing service experiences across communication
channels and activate long term health motivation and -behav-
iours, in order to deliver long term results. This is reflected in
the following design evaluation framework for health im-
provement ICT solutions [25], see Fig. 1. It evaluates the
impact of the ICT-enabled intervention on health effective-
ness, coaching performance and ICT value adding.
To increase solution impact, a hybrid or multi-channel ser-
vice mix is recommended [29,28,22–24], combining elec-
tronic and face-to-face interactions. For example, face to face
‘on site’coaching had as benefits: a richer service experience
Health Technol.

with the coach, with other participants and with a health fo-
cused ‘service scape’; group support experiences (obtaining
additional social support and co-creating service experiences
together); learning from each other; health experiences in
healthy food-, sports- and relaxation exercises. Disadvantages
are: more (travel) time needed; less flexibility regarding when
and where; and not everyone likes group sessions [5]. Elec-
tronic and (semi-)automated coaching has as benefits: more
time-efficient; more flexibility in when and where to have
contact; very explicit monitoring of your own progress online;
having status reports including ‘next steps’commitments al-
ways online. Disadvantages are: the sensory-, emotional- and
group experiences are more limited. Also, the ‘service scape’
in which people are immersed is only virtual, not physical. In
summary, often a hybrid service mix has most to offer.
In such a hybrid service mix, mobile micro-learning could
potentially offer a number of advantages, namely, it uses a
personal device that is available any time any place, it is effi-
cient and can use idle time that is otherwise lost, and it is
suited for just in time learning [3].
3 Methods and materials
Regarding our design research approach, we follow the design
cycle methodology provided by Vaishnavi & Kuechler
W.[28]) which goes from problem awareness and solution
suggestion to development, evaluation and conclusion. After
reporting our multiple-case study results in section 4, we will
discuss design lessons in section 5.
3.1 Intervention: hybrid lifestyle intervention with mHealth
App
The mHealth Health Quiz App was used within a hybrid
(face-to-face and digital) service mix. The healthy lifestyle
support service mix consisted of:
–Digital health behaviour surveys ‘BRAVO’with automat-
ed personal feedback (on physical activity, smoking, al-
cohol, food and energy/recuperation), at 0, 1, 3 and
10 months.
–A 3-hour start up workshop (with about 30 participants)
–Personal action plan drawn up at the start workshop
–Asking participants to use weekly buddy contacts
–The mHealth Health Quiz App (available for Android,
iOS, blackberry and laptops)
–Supporting health education materials via email
–A 2-hour repeat workshop after 1 month for answering
questions and for (peer) education
–Twelve times a weekly health tip email to help maintain
awareness and motivation
Our Health-Quiz App was based on the micro-learning
principles and technology platform as outlined previously
[3]. On this platform, learning occurs via micro learning cards,
each containing a question, (mostly) multiple choice answer
options, plus a brief explanation after each answering attempt.
The learning cards are organized in a number of courses and
participants can see how much they progressed within a
course. The course content was designed with seven course
modules of about 20 questions each. The first three courses
educate participants on the ‘what’or basic knowledge of
healthy food-, exercise- and mental energy behaviours. The
fourth course provides education and examples on what de-
termines long term health behaviour success. Courses five
through seven educate participants on the ‘how’or daily tac-
tics of healthy food-, exercise- and mental energy behaviours.
Within the course content design, questions are ranked in a
certain order to address consecutive learning objectives. First,
basic knowledge and awareness are increased, then motiva-
tion and plan making, next there is support for daily activities
and coping strategies, plus seeking new self-norms and self-
identity. Participants are free to switch between courses in
order to support ‘just in time’learning based on their needs.
Our Health Quiz Apps were downloaded and activated for all
users during the 3-hour start up workshop.
3.2 Multiple-case study in three employer organizations
From Feb to Dec 2013, a multiple-case study was conducted
with three employers to evaluate real world impacts of the
healthy lifestyle intervention in Dutch work settings. The em-
ployers were: a Municipality (n=30 participants), an
-Health literacy
- Health beh aviors
- Health ou tcomes
- Quality of life and well-being
ICT value adding:
- Qu ality o f moti vators, tri ggers, experi ence s
- Simplicity: f amiliar interfaces, ease of use
- Embedded in and en hancin
g
coach relation
Coaching performance:
- Promoting health actions
- Supporting self-efficacy
- Activatin
g
intrinsic motivation
Health effectiveness:
Fig. 1 Basic requirements when
designing ICT-supported healthy
lifestyle interventions
Health Technol.

Advocacy organization for Dutch senior citizens (n=26 par-
ticipants, half of them volunteer workers) and a Care Provider
(n=30 participants).
From the perspective of these three organizations, an im-
portant question was: does this lifestyle intervention generate
health behaviour improvements in the short and longer term?
This was assessed with a standardized Dutch online ‘BRAVO’
survey (on physical activity, smoking, alcohol, food and ener-
gy/recuperation), at 0, 1, 3 and 10 months. Moreover, at
months 1, 3 and 10 several additional health behaviour and
health readiness (according to the stages of awareness, will-
ingness, plans and actions) questions were asked on top of the
brief BRAVO question set.
Next, a design evaluation survey was added at month 1,
and the findings from this survey were discussed in the 1-
month group workshops at each organization, to obtain qual-
itative insights into the why and how of the survey data and
user experiences. The design evaluation survey addressed: 1)
an evaluation of health promotion value of the different ele-
ments in the service mix, including the Health Quiz App, 2)
the micro-learning mApp usefulness and ease of use. Besides,
logging was conducted of use and progression in the micro-
learning mApp environment.
4Results
The overall patterns were rather similar across the three em-
ployer organizations regarding: adoption, evaluation and ef-
fects of the healthy lifestyle intervention. Hence, we first
Tabl e 1 Health behaviours distribution at start and 10 months
Green = health norm compliant. Yellow = nearly compliant. Red = the rest
Health Technol.

describe the generic outcomes. After that, we report cross-case
differences.
At the start, there were 86 participants (n=30/26/30 for
Municipality, Advocacy and Care Provider respectively).
The survey response rates were 74 % (n= 64) at 1 month, then
55 % (n=47) at 3 months and 56 % at 10 months (n=48 for the
BRAVO questions, n=47 for the rest).
4.1 Generic outcomes across the three employer organizations
From the perspective of the three employer organizations, the
key outcome is that health behaviours improved after using
our Health-Quiz app, as measured with the standardized
Dutch BRAVO survey (on physical activity, smoking, alco-
hol, food and energy/recuperation). As expected, the main
improvement in health behaviour patterns occurs in the first
month. After that, the new pattern is relatively stable, at least
for the 10 months period we measured. This pattern was
observed within each organization. Table 1provides the be-
haviour distribution accumulated for the three organizations at
the start and 10 months. Table 2summarizes the number of
worsened and improved scores at months 1 and 10, compared
to the start. Green means: compliant with Dutch health norms,
yellow is nearly compliant and red is the rest.
Additional behaviour improvements reported in the sur-
veys were (we list how many of the n=47 respondents
(strongly) agreed at 10 months): I am physically active more
often during the day (72 %), I take a relax moment more often
(47 %), I more structurally reduce my stress sources (51 %), I
eat fewer sugars and refined carbohydrates (55 %), I eat more
whole meal plant foods like nuts, mushrooms, olives etc.
(81 %), I eat less red and processed meat (57 %), I eat fewer
butter fats (60 %). Next, the health readiness indicators im-
proved (awareness, intentions and plans), plus health effects in
terms of improved physical or mental fitness. The scores at 1,
3 and 10 months were very similar. Table 3provides the
10 months results (n=47).
Next, a question was: how did the Health Quiz App and
other service mix elements contribute to the results above?
This question was part of the 1-month evaluation. In the sur-
vey data as well as the workshops it was observed that not
everybody had preferences for the same service elements.
Still, most service elements were judged to be helpful for
improving health behaviours by a significant part of the users,
see Table 4. The lowest helpfulness scores as percentage
‘(strongly) agree’,n=64, were given for: doing it as a group
(20 %), having a buddy (25 %) and the weekly health tip mail
(36 %). The highest helpfulness scores as percentage ‘(strong-
ly) agree’,n=64, were given for: having a second workshop at
1 month (52 %), feeling better (53 %), the Health Quiz mApp
(53 %), making my own health activity plan (66 %), knowing
what my own influence is (75 %) and the start workshop
(77 %).
A next set of evaluation questions at 1 month regarded
usefulness, added value and ease of use of the micro-
Tabl e 2 Summary of worsened and improved behaviour scores at 1
and 10 months (irt start)
Number of people,
at 1 month (n=64)
Number of people,
at 10 months (n=48)
Health Behaviours: Worsened: Improved: Worsened: Improved:
Moderate Physical
Activity
419414
Intensive Physical
Activity
421418
Smoking 2 2 1 3
Alcohol 4 8 1 8
Food, Vegetables 2 23 3 16
Food, Fruits 5 32 1 18
Food, BMI: 2 4 1 5
Recuperation/Relaxing 5 16 3 14
Energy 5 17 5 14
Tabl e 3 Health readiness and fitness improvements after 10 months, n=47
Green = (highly) agree. Yellow = neutral. Red/orange = (highly) disagree
Health Technol.

learning Health Quiz mApp. The majority of respondents in-
dicated that it was efficient, useful, fun, that they would have
learned less without it and that they now made healthier
choices thanks to the micro-learning Health Quiz mApp. Like-
wise, the majority also indicated that it fulfilled a desire to
learn more after the start workshop, that it was low effort,
that the (smaller) mobile screens were no hindrance, that
most learning cards were relevant, that things were learned
that were directly applicable and that the courses provided
regular stimuli to make healthy choices. These findings
were confirmed in the feedback during the 1-month
workshops.
Moreover, the qualitative findings were corroborated via
the micro-learning logging data, in the sense that the majority
of participants who started the micro-learning Health Quiz
mApp also completed all 7 available courses. In contrast with
the eHealth law of attrition [7], completion rates were relative-
ly high. On an intention to treat basis, 66 % of the courses
were completed that were made available for the n=86
starting participants.
4.2 Cross-case differences
There were a few case-specific characteristics that had an im-
pact on adoption patterns and compliance and response rates.
However, the overall design evaluation findings were very
similar across cases. In Table 5we highlight the differences
and below we list the differences in course completion rates,
based on microlearning logging data.
In the Municipality case (n=30), 14 participants completed
all seven courses, five completed several courses and 11 com-
pleted none (of which 7 participants never started the micro-
training). The total completion rate was 53 % of all available
course material. In the Advocacy case (n=26), 18 participants
Tabl e 5 Cross-case differences
Employer case Case-specific characteristics and findings
Municipality - Relatively lower start- and completion rates,
partly due to the fact that several participants
were sent by their managers and did not
participate on a voluntary basis.
- For those who did participate, the relative
improvements in eating vegetables and
adding moderate intensity physical activity
were larger than in the other cases.
Advocacy - About half of the group were (senior)
volunteers and the average age in this
group was highest. Several sudden
events happened in the lives of these
participants, hampering workshop
participation and course completion
for several of them.
-Initial health behaviour scores were
highest in this group: for daily physical
activity, for fruit and vegetables
consumption. However, their energy
was lower and stayed low.
- About 50 % were retired: some were
more busy than ever. Others reported
that the topics of work related stress
and energy were less relevant for them.
Care Provider - The regional director was a strong
health advocate, participant and
initiator in this group. Start- and
compliance rates were highest is this group.
- This group had the lowest start fruit
consumption, but the highest final score.
- The improvements were largest in energy
and recuperation behaviours in this group,
as well as the reported gains in mental
fitness (at 1, 3 and 10 months).
Tabl e 4 Service elements that stimulated healthier behaviours (n=64, at 1 month)
Green = (highly) agree. Yellow = neutral. Red/orange = (highly) disagree. Grey = not applicable
Health Technol.

completed all seven courses, and eight completed none (of
which five never started the micro-training). Their total com-
pletion rate was 69 % of all available course material. In the
Care Provider case (n=30), 21 participants completed all sev-
en courses, fourcompleted several courses and five completed
none (all of them did start the micro-training and completed
multiple questions, mostly sampling them across multiple
courses). The total completion rate was 75 % of all available
course material.
5 Discussion and conclusion
In conclusion, the short answer to all four research
(sub-)questions in this paper was yes, and we will discuss
the answers to the research questions in relation to the evalu-
ation framework of Fig. 1. First, the mobile health quiz pro-
vides added value (usefulness, fun, positive triggers) with low
barriers (ease of use, limited time burdens). Second, according
to the participants the mobile health quiz is well integrated in
the overall service mix. Hence, the ‘ICT effectiveness’factor
of Fig. 1appears to be sufficiently addressed. The mobile
health quiz also increases the ‘coaching effectiveness’factor
of Fig. 1by providing regular triggers for health awareness,
coping strategies and useful health behaviours. In answer to
the third research sub-question, health readiness (awareness,
motivation, plans and actions) and competence (health percep-
tions, everyday choices, coping and goal achievement, growth
and development) are improved. Fourth, the various health
behaviours are improved as measured with the Dutch BRAVO
survey (on physical activity, smoking, alcohol, food and ener-
gy/recuperation).
The answers to the third and fourth research sub-questions
demonstrate the contribution to the ‘health effectiveness’fac-
tor of Fig. 1, both of the overall hybrid service mix and of the
mobile micro-learning health quiz within that mix. Besides
explicit participant feedback, we have the indications from
the course completion rates of the mobile health quiz, which
were 66 %: well above the Eysenbach [7]‘law’. Given the fact
that these courses were not mandatory for these participants,
but only additional support for health self-management, and
given the fact that each course easily takes 20 min to
complete (in a context of time scarcity, [3]) we regard
course completion rates as a sensible proxy for perceived
usefulness.
Health behaviours not only improved at 1 and 3 months,
but also 10 months after the start. The latter finding is rela-
tively special, in the sense that most healthy lifestyle interven-
tions, whether at work sites [29]ornot[17], tend to generate
only short term results (3 or 6 months), after which people
generally fall back into their old patterns.
These long term effects might have been promoted by a
positive peer support effect from other group members [15]or
management (see ‘Care Provider’case in Table 5), even
though this was not mentioned as a strong factor by most
participants: see last item of Table 4.
Tabl e 6 Design evaluation (authors’opinions, 5-point scale from - - to ++)
Health Effectiveness Coaching Performance ICT Value Adding
++ Health Literacy: Impacts from Health
Quiz, workshops and education
materials.
+ Health behaviours: BRAVO survey
indicates improvements.
+/−Health outcomes: Feeling more fit is a
positive outcome, but more objective
measures not used.
+ Quality of Life: Feeling better, mentally
and physically more fit.
+/−Promoting health actions: Many health
tips are provided. Impact depends on
execution of plans.
++ Supporting self-efficacy: Users indicate
astrongcontributionfrom‘know what
my own influence is’
++ Activating intrinsic motivation: A
strongly activated desire to improve, plus
rewards via feeling better.
+ Motivators, triggers, experiences: health quiz, mail tips and
surveys provided triggers, (fun) experiences plus hope and
improvement opportunities.
+/−Simplicity: Installation and first use were burdening for
some. After that, usage was simple, low effort.
+/−Fit with coach processes: Users felt synergies with the
workshops, education materials, personal action plans and
answering individual questions.
Potential improvement:
Using more objective health outcomes,
possibly future 24×7 health tracking.
Potential improvement:
Context aware and personalized coaching.
Potential improvement:
Coach processes could be automated more (e.g., goals/means
support).
Fig. 2 Health competence pyramid for long term health
Health Technol.

5.1 Theory
Our contributions to theory are threefold. Through this empir-
ical test of our proof of concept with employer organizations
we see a tentative confirmation of the three design research
propositions on which we have built our design research. First,
a mobile micro-learning health quiz appears useful for fulfill-
ing the key design requirements from Fig. 1when designing
ICT-supported healthy lifestyle interventions: health-,
coaching- and ICT effectiveness.
Second, as contributions on these design requirements
increased, we indeed empirically observed improved
health readiness, behaviours and competence. Thus, our
second proposition is tentatively confirmed that the design
requirements from Fig. 1may contribute to ICT-enabled
health intervention success.
Third, long term health behaviours appear to benefit
from a grounding in health competences (health percep-
tions, everyday choices, coping and goal achievement,
growth, health identity and self-evaluation), see Fig. 2.
This is largely a qualitative observation, based on partic-
ipant feedback and sensitized by the work of Kahneman
[12]andSeligman[18] on daily routines, decisions,
growth, and the way happiness and life satisfaction are
grounded in identity and self-evaluation, see also the the-
ory section. Long term health impacts seem to lie not only
in health readiness (awareness, intentions, plans, actions –
which have a relatively operational focus and are aimed at
next week rather than next year, see the HAPA and i-
change models from theory). Rather, longer term health
competences can be trained and developed: from health
perceptions, via everyday choices, strategies for coping
and goal achievement, growth, to health identity and
self-norms. This is also what several study participants
indicated: their health views had changes, as well as their
preferences, choices, health goals, self-management and -
evaluation. They indicated this is what helped them deal
with the changing dynamics of life and health in the lon-
ger run. We have planned additional research in order to
more rigorously measure the direct contributions of
eCoaching to health competence development.
5.2 Limitations and practical implications
Regarding practical implications, the relevant design question
is what made these results come about and how can we im-
prove even further? After discussing study limitations we con-
duct a design evaluation, using the framework from our theory
section.
This study has several limitations. First, it is largely quali-
tative, evaluating effects across three case organizations. In
the survey, users mostly agree on issues of micro-learning
perceived usefulness and perceived ease of use: both
antecedents of the TAM model [4]. Statistical techniques like
explorative regression analysis were not possible due to small
sample size and low variance. Second, our survey results are
likely subject to self-selection biases: our survey respondents
were self-selected consisting of largely of users who complet-
ed most or all of the micro-learning courses. They are the ones
most likely to be biased positively. Third, the context is dif-
ferent in each case organization and in our study design we
cannot control for confounding factors. On the other hand, the
strong evaluation- and effect agreement between participants
across organizations does hint at the robustness and cross-case
validity of the findings.
Tab le 6summarizes results from our study and dis-
cusses methods for improvement for our design. Regard-
ing factor 1 of Fig. 1, health effectiveness, more objective
improvement measures could be advantageous. Especially
for self-management of people with health issues (e.g.,
diabetes-2, irregular high blood pressure or heart arrhyth-
mia, or impaired renal function) 24×7 monitoring of ef-
fects of lifestyle improvement can be very beneficial. For
the second factor, coaching effectiveness, there is a chal-
lenge of automatically integrating context- and health in-
formation. For example, if we notice that someone has
been sitting most of the day, when will reminders/
triggers be appreciated to get up and move about, and
when not? For example, if I’mverybusywithfinishing
a report or having urgent meetings, it can be a conscious
and preferred strategy to continue a sedentary work activ-
ity for the time being. Reminders and triggers can also
become a nuisance. But at other times, when falling in
my coach potato trap in the evening, I may very well
appreciate more persistent triggers to go play sports with
a buddy. Finally, ICT value adding (factor 3) could be
improved via at least two routes. First, improving auto-
mated logging of health (behaviour) data and integrating
this into coach processes. Second, designing more intelli-
gent, interactive coach processes, which incorporate user
preferences and plans, contextual/situational priorities and
health data consequences.
In summary, given the relatively static content of the
micro-learning health quiz, it served its health support
goals well, thanks to the other service mix elements
and the overall service concept. eCoach improvement
opportunities for the future abound, of which we identi-
fied several.
Conflict of interest L Simons is director of the Health Coach Progam, a
service provider that developed part of the solution tested in this research.
P Bruck is director of Research Studios Austria Forschungsgesellschaft
mbH, a service provider that developed part of the solution tested in this
research.
Authors F Foerster, L Motiwalla and C Jonker declare that they have
no conflict of interest.
Health Technol.

Open Access This article is distributed under the terms of the Creative
Commons Attribution License which permits any use, distribution, and
reproduction in any medium, provided the original author(s) and the
source are credited.
References
1. Anderson J, Parker W, Steijn NP, et al. Interventions on diet and
physical activity; what works. Summary Report. Geneva: WHO;
2009.
2. Baumeister RF, Tierney J. Willpower: rediscovering the greatest hu-
man strength. New York: Penguin Group; 2011.
3. Bruck PA, Motiwalla L, Foerster F. Mobile Learning with Micro-
content: A Framework and Evaluation. Paper presented at the 25th
Bled eConference. 2012 from www.bledconference.org.
4. Davis FD. Perceived usefulness, perceived ease of use, and user
acceptance of information technology. MIS Q. 1989;13(3):319–40.
5. Demark-Wahnefried W, Clipp E, Lipkus I, Lobach D, et al. Main
outcomes of the FRESH START trial: a sequentially tailored, diet
and exercise mailed print intervention among breast and prostate
cancer survivors. J Clin Oncol. 2007;25(19):2709–18.
6. De Vries H, Mudde A. Predicting stage transitions for smoking ces-
sation applying the attitude –social influence –efficacy model.
Psychol Health. 1998;13:369–85.
7. Eysenbach G. The law of attrition. J Med Internet Res. 2005;7(1):
e11.
8. Fogg BJ. Persuasive technology: using computers to change whatwe
think and do.BUbiquity, December (2002): 5.
9. Fogg BJ. A behavior model for persuasive design. Proceedings of the
4th international conference on persuasive technology. ACM, 2009.
10. Friedman HS, Martin LR. The longevity project: surprising discov-
eries for health and long life from the landmark eight decade study.
New York: Hudson Street Press; 2011.
11. Jimison H, Gorman P, Woods S, Nygren P, Walker M, et al. Barriers
and drivers of health information technology use for the elderly,
chronically ill, and underserved. Evid Rep Technol Assess (Full
Rep). 2008;175:1–1422.
12. Kahneman D. Thinking, fast and slow. London: Penguin; 2011.
13. Leerlooijer JN, Ruiter RA, Reinders J, Darwisyah W, Kok G,
Bartholomew LK. The world starts with me: using interventionmap-
ping for the systematic adaptation and transfer of school-based sex-
uality education from Uganda to Indonesia. Trans Behav Med.
2011;1(2):331–40.
14. Lippke S, Wiedemann AU, Ziegelmann JP, Reuter T, Schwarzer R.
Self-efficacy moderates the mediation of intentions into behavior via
plans. Am J Health Behav. 2009;33(5):521–9.
15. Pender NJ, Murdaugh CL, Parsons MA. The health promotion mod-
el. Health promotion in nursing practice, 2002: 4.
16. Schwarzer R. Modeling health behavior change: how to predict and
modify the adoption and maintenance of health behaviors. App
Psych: An Int Rev. 2008;57(1):1–29.
17. Seidell J, Halberstadt J. Tegenwicht: feiten en fabels over
overgewicht. Amsterdam: Bert Bakker; 2011. p. 232.
18. Seligman ME. Flourish: a visionary new understanding of happiness
and well-being. New York: Simon and Schuster; 2012.
19. Simons LPA, Bouwman H. Designing a click and mortar channel
mix. Int J Internet Mark Advert. 2004;1(3):229–50.
20. Simons LPA. Multi-channel services for click and mortars: develop-
ment of a design method. PhD Thesis, Delft University of
Technology. 2006.
21. Simons LPA, Hampe JF. Service Experience Design for Healthy
Living Support; Comparing an In-House with an eHealth Solution.
Paper presented at the 23rd Bled eConference. Bled, Slovenia, 2010.
from www.bledconference.org.
22. Simons LPA, Hampe JF. Exploring e/mHealth Potential for Health
Improvement; A Design Analysis for Future e/mHealth Impact.
Paper presented at the 23rd Bled eConference. Bled, Slovenia,
2010b. from www.bledconference.org.
23. Simons LPA, Hampe JF, Guldemond NA. Designing Healthy
Consumption Support: Mobile application use added to (e)Coach
Solution. Paper presented at the 25th Bled eConference. Bled,
Slovenia, 2012. from www.bledconference.org.
24. Simons LPA, Hampe JF, Guldemond NA. Designing healthy living
support: mobile applications added to hybrid (e)coach solution. Heal
Technol. 2013;3(1):1–11.
25. Simons LPA, Hampe JF, Guldemond NA. Designing ICT-support for
healthy lifestyle interventions. Appear Electro Markets. 2014. doi:10.
1007/s12525-014-0157-7.
26. Simons LPA, Steinfield C, Bouwman H. Strategic positioning of the
Web in a multi-channel market approach. Internet Res. 2002;12(4):
339–47.
27. Sperling R, Simons LPA, Bouwman H. Multi-channel service con-
cept definition and prototyping. Int J Electron Bus. 2009;7(3):237–
55.
28. Vaishnavi V, Kuechler W. Design Research in Information Systems.
Last updated August 16, 2009 from http://desrist.org/design-
research-in-information-systems.
29. Verweij LM, Coffeng J, van Mechelen W, Proper KI. Meta-analyses
of workplace physical activity and dietary behaviour interventions on
weight outcomes. Obes Rev. 2011;12:406–29.
Health Technol.