Human Energy Diary Studies with Personalized Feedback:
A Proof of Concept with formr
Fabienne Lambusch
, Henning Dirk Richter
, Michael Fellmann
, Oliver Weigelt
and Ann-Kathrin Kiechle
Business Information Systems, University of Rostock, Rostock, Germany
Institute of Psychology, Wilhelm Wundt, Leipzig University, Leipzig, Germany
Work and Organizational Psychology, University of Hagen, Germany
Keywords: IT-based Intervention, Momentary Assessment, Personalized Feedback, Self-reflection, Energetic Wellbeing.
Abstract: While the current pandemic amplifies the trend of highly self-responsible and flexible work, many employees
still struggle addressing the resulting self-management challenges like balancing strain and recovery.
Maintaining health of employees is a major concern of organizations to remain competitive, but in the context
of highly individual work, this can hardly be supported with classical occupational health initiatives. Thus, it
is crucial to develop tools that provide individuals with personal insights on their everyday work and help
them determine applicable health behaviors. Towards this goal, we report on our design and implementation
of diary studies with personalized feedback about persons’ energetic wellbeing. Whereas such studies enable
to research phenomena at the collective level, they can additionally act as intervention at the individual level.
This is especially relevant to 1) provide a motivational incentive for continued participation and 2) raise
awareness about recent topics in occupational health and promote healthy behaviors, while advancing research
concerns. We provide insights from several studies regarding the generated feedback, the perception of the
participants and IT-related improvement potentials. Hopefully, this will inspire further research that takes
advantage of the win-win situation conducting studies, which simultaneously provide participants with
individual insights.
In the past decades, working conditions shifted more
and more towards complex and knowledge-intense
tasks, increased expectations for flexibility, and high
speed (Green and McIntosh, 2001; Parent-Thirion et
al., 2017; Biletta et al., 2021). Thus, managing
balance in life became more challenging for
individuals (Green and McIntosh, 2001; Barber and
Jenkins, 2014). In this context, the so-called
human energy plays a major role. Quinn et al. (Quinn
et al., 2012) describe human energy as an
organizational resource that increases employees’
ability to act by motivating them to do their work and
achieve their goals. Human energy is an umbrella
term that comprises physical aspects, like the
available glucose in the blood enabling humans to act,
and subjective aspects, like the degree of feeling
alive. Quinn et al. call these two components physical
energy and energetic activation and present an
integrated model of human energy at work that can be
seen in Figure 1. Yet, research provides only scattered
indications of which factors influence especially the
subjective component of energetic activation and how
an employee can proactively improve energy
management on an individual level (i.e. energy self-
management). Although prior research investigated
the fields of job design (Grant and Parker, 2009),
leaderships (Inceoglu et al., 2018; Skakon et al.,
2010) and interventions (Tetrick and Winslow, 2015)
in order to foster employee wellbeing, addressing
self-management challenges via digital solutions has
Lambusch, F., Richter, H., Fellmann, M., Weigelt, O. and Kiechle, A.
Human Energy Diary Studies with Personalized Feedback: A Proof of Concept with formr.
DOI: 10.5220/0010974100003123
In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF, pages 789-800
ISBN: 978-989-758-552-4; ISSN: 2184-4305
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Figure 1: The integrated model of human energy over time in a work context (Quinn et al., 2012), which is a theoretical basis
of the presented studies with feedback on human energy.
not yet received much attention (Li and Vogel, 2021).
Self-management is key to find and improve the own
individual way to perform (Drucker, 2005). It means
controlling the own actions in a way that prefers
behaviors with consequences desirable in the longer-
term over short-term outcomes (Manz and Sims,
1980). Self-management skills are essential for work
characterized by high degrees of freedom (Kleinmann
and König, 2018). In order to manage oneself, several
strategies can be used. Self-observation, where a
person systematically gathers data about the own
behaviour (Manz and Sims, 1980), is an exemplary
strategy that is especially relevant in our context.
Indeed, designing and implementing IT-based tools
that support employees in the collection and analysis
of data relevant for self-reflection is a promising
avenue of research (Choe et al., 2017; Rapp and Cena,
2014; Fallon et al., 2018). Specifically for human
energy, there is yet no technical support assisting
individuals in identifying how different factors like
micro-breaks (Kim et al., 2018) influence their energy
level. Determining the influencing factors that are
particularly relevant within the own working day
would be highly valuable in order to proactively
increase the own energy level or prevent a decrease.
Diary studies help to regularly gather data about
peoples’ situation, especially if there is no established
automatic measurement instrument like sensors for
the targeted phenomena yet. They provide gaining
insights over a certain period of time by requiring the
participants to submit protocols of their activities
independently and frequently (Janssens et al., 2018).
The character of a diary study enables combining
research with the provision of early and individual
feedback to the participants of the studies, even
before the detailed scientific analyses take place that
focus more on generalizable results. Overall, diary
studies are very reasonable to keep track of dynamics
in experiences of and between employees in
organizations (Ohly et al., 2010). As diary studies can
require much time from the participants depending on
how frequently and deeply they are asked to assess
their situation, providing individual feedback may
raise the intrinsic motivation for regular participation
(Vries et al., 2021). With this, the participants expect
and receive insights, they are likely interested in.
Through generating personalized feedback on human
energy during work days, we furthermore strive to
empower employees to better understand their energy
and improve their management in such a way that
enables overload prevention and lasting work
pleasure. This would create added value for the
individual as well as the organization, which in
addition might lead to a better feasibility of
implementing diary studies for research purposes in
organizational contexts.
However, the design and implementation of IT-
supported diary studies with personalized feedback
remain challenging in terms of the technical
infrastructure required and the existing sample cases
described in sufficient detail to learn from. In
addition, there is also a lack of research how
participants perceive personalized feedback in diary
studies. Against this gap, we report on the design,
implementation and execution of our IT-supported
diary studies on human energy using the established
tool formr. Our results thus can inform the design and
implementation of future IT-supported diary studies
that emphasize personalized feedback.
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In this section, we provide information on related
studies and tools starting with ambulatory assessment
studies more generally and proceeding with
electronic diary studies with feedback and the digital
tool we used for our diary studies.
2.1 Ecological Momentary Assessment
and Intervention Studies
A term commonly used in diary research is ecological
momentary assessment (EMA), which includes
diverse ambulatory assessment methods (Janssens et
al., 2018). EMA refers to methods involving repeated
sampling of subjects' current behaviors and
experiences in real time (= “momentary”) in the
natural environment (= “ecological”). Thus, EMA
comprises not only methods using diaries, but also
such using e.g. physiological sensors (Shiffman et al.,
Existing electronic EMA studies are often
focused towards researching interesting phenomena
such as (clinical) symptoms, behaviors or perceptions
and their interplay. For example, there are numerous
studies that focus on understanding basic
psychological need fulfillment at the workplace, as
summarized by Coxen et al. (2021) in their analysis
on 20 diary studies. Giving participants feedback is
not at the heart of such studies. Rather, data is
collected for understanding and gaining scientific
knowledge about the phenomenon under
consideration. Vries et al. (2021) focus in their review
on smartphone-based EMA studies on wellbeing and
explicitly recommend providing feedback to the
subjects at the end of the study in order to motivate
them for continued participation. Even though about
half of the analyzed 53 smartphone-based EMA
studies additionally integrate passive sensor data,
nearly all studies also use the collected data for their
research analyses only. The review mentions just one
exemplary study, in which participants got feedback
in form of personalized graphs about their happiness.
We will look at this study in the next section, as its
approach is quite similar to what we propose.
In addition to the more insight-oriented studies
described so far, there are also intervention-oriented
studies. In the mobile context, such studies aim at
delivering just-in-time prompts as treatments, as
indicated in a review on 27 ecological momentary
intervention (EMI) studies with mobile technology
support (Heron and Smyth, 2010). This sort of
feedback often is directive in its nature and presented
e.g. in the form of small textual messages.
Alternatively, interventions are offered by questions,
conversational interaction, or multimedia content as
described in a review study on 64 EMI studies by
Balaskas et al. (2021). Four of the analyzed studies
actually provided participants feedback in form of
graphical data visualizations of past entries. These
studies are addressed in the next section together with
others including visual feedback. However, the
feedback provided seems to be a bye-product of the
actual goal to deliver and research momentary
interventions that are used as treatments and is often
just roughly mentioned. In contrast to EMI designs,
we propose to utilize the integration of rich
visualizations of participant data for reflective
purposes and higher participation motivation even for
studies that have mainly an assessment character and
do not necessarily aim at intervening in opportune
To summarize, while previous work mostly
focused on insight-oriented or intervention-oriented
studies, we specifically focus on a study type between
these that enables assessments for research purposes,
but includes a reflective benefit for the participants
providing rich and personalized feedback. Besides the
benefit for participants, this approach also provides
the perfect basis to evolve an insight-oriented study
later into an intervention-oriented study using the
feedback as an intervention for reflection or adding
other interventions. This would also promote the
connection of EMA and EMI techniques that
remained largely separate, but would enable better
tailoring and delivery of interventions (Heron and
Smyth, 2010). In the next section, we analyse the few
works that are closer to our approach by providing
reflective visual feedback on the collected data.
2.2 Electronic Diary Studies with
Feedback Generation
According to Narciss (Narciss, 2006), feedback is an
information given to a person during or after a process
in order to have a regulating effect on that process.
Zannella et al. (Zannella et al., 2020) state a beneficial
effect caused by feedback, if used cautiously. They
argue that providing participants with personalized
feedback may not be generally feasible, especially
where results can be sensitive or easily misinterpreted
as a wrong psychological diagnosis. Thus, they
suggest carefully deciding which captured data is
considered for feedback and how it is presented to
decrease the risk of misconstruing.
Unfortunately, many research documentations
about diary studies with feedback generation neither
describe the design nor the impact of the generated
Human Energy Diary Studies with Personalized Feedback: A Proof of Concept with formr
feedback. The authors then just mention that feedback
was provided for the participants, but do not explain
more on that (Rentzsch et al., 2021; Richter and
Hunecke, 2021; Arslan et al., 2019a; Arslan et al.,
2019b; Holzleitner et al., 2017; Pusch et al., 2020;
Depp et al., 2015; Kazemi et al., 2019).
Few works at least shortly describe the feedback
they generated for their participants. For example,
Burns et al. (2011) provided participants visual
feedback related to depression, e.g. a graph showing
the frequency of the locations they were at together
with their average reported mood in each location.
Kroska et al. (2020) developed an application for
assessment and intervention in their study that can
visualize data collected regarding mood and activity.
Participants can access graphs e.g. on their depressive
symptoms, perceived stress symptoms, or certain
behavior over three days. Advanced visual feedback
on health and wellbeing was provided to participants
in the study by van der Krieke et al. (2017). Besides
some rather basic graphs like frequency of certain
activities ranked by perceived pleasantness, also
personal networks showing concurrent and dynamic
relationships between mood, health behaviors, and
emotions over time were presented to participants.
While the aforementioned studies can well inspire the
design of feedback to be generated for the
participants, they all lack describing their technical
infrastructure and corresponding study design in
sufficient detail for reuse. Researchers conducting
EMA studies often use applications, which were
specifically developed for their research and thus the
development costs a lot of time and money (Vries et
al., 2021). For studies with feedback generation, it is
even more important to build on an existing
infrastructure to reduce complexity of
implementation. Non-commercial tools that provide
functionality for conducting a diary study as well as
generating comprehensive personalized feedback
while fulfilling research demands (e.g.,
reproducibility, traceability, privacy guaranteed or
extensibility), are still rarely found. Furthermore,
non-commercial software is often poorly maintained
due to limited resources (Arslan et al., 2019c).
Arslan et al. (2019c) developed a study
framework and an open-source software tool that
tackles this gap, namely formr (see next section for
more information). They describe in their paper three
case studies with automatized feedback illustrating
the capabilities of their tool. One exemplary diary
study with personalized feedback aimed to
investigate daily habits and sexuality of women over
a period of 70 days. The participants received various
personalized feedback at the end of this study. In
addition to personality feedback, the study provided
them with visualizations of the variation of their
mood, desire and stress level during their menstrual
cycle. The participants could even investigate several
visualized correlations between the quality of their
sleep and mood level and their alcohol consumption
on the previous day. Additionally, an interactive
display provided the participants the possibility to
retrace their mood level over time and investigate
their answers from a specific day. Moreover, the
participants were also provided with a spider diagram
showing the distribution of activities in portions
during the week and the weekend.
Conducting a study with formr that uses diverse
of its features, is still challenging due to the
complexity of possibilities and the still rather short
information on exemplary cases. With this article, we
contribute an exemplary case with descriptions of
study designs, implementation choices, participant
perceptions, technical challenges, and learnings from
our study on human energy, specifically focusing on
combining EMA and personalized feedback. We
provide with this a proof of concept for future studies
and hope to reduce barriers other researchers may
face when conducting a similar study.
2.3 formr – A Tool for Diary Studies
Arslan et al. (2019c) developed formr, a study
framework and an open-source software tool
supporting researchers in conducting a wide range of
studies (i.e., from simple surveys to even more
intricate research). Thereby, it allows to
automatically send email or SMS notifications to
registered participants. Researchers can thus
determine a specific time schedule formr follows. The
notifications embody an external trigger to remind
and motivate participants to do their self-assessments.
Furthermore, formr supports the coding language R
to execute more complex tasks like generating
personalized feedback. Through coding in R, a wide
range of different visualizations can be created for the
feedback. For instance, a participant’s data can be
shown in a table, pie chart, bar chart, line graph or
radar chart.
Overall, the formr framework consists of three main
elements: 1) the survey framework, 2) the study
framework (aka “run”) and 3) the R package. In the
survey, researchers can define questions and to
correspondingly gather data from participants. The
“run” of provides researchers the possibility to
actively manage and drive the survey (i.e.,
researchers can manage access to a study, define
when which questions are answered by whom, send
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emails or text messages to remind or invite the
participants and provide feedback to the users).
Whereas those two main components of formr are
coded in PHP, the third one is the utility R package
und thus independent from the other PHP code. This
should ensure common operations (like cleaning and
aggregating data or setting timeouts for analyzing
purposes) becoming easier to implement for the
researchers. The R package is connected to the PHP
software via a RESTful API allowing researchers to
use many familiar packages directly in formr (e.g., for
displaying graphical feedback to the users. Overall,
those features seem to perfectly fit the requirements
of performing longitudinal studies and thus also diary
studies. That is, data can be gathered from
participants by creating surveys, the execution of
those surveys can be maintained in the runs (e.g., by
reminding inactive users to continue participating)
and the gathered data can be precisely analyzed
afterwards using the R packages.
Based on positive response of participants in first
studies, we conducted three diary studies on human
energy over the last three years where we combined
researching human energy in terms of energetic
activation and its influencing factors with developing
a flexible study procedure and valuable feedback for
the participants of the studies. In this, we iteratively
improved the personalized feedback provided to the
participants and added more and more complexity to
it in order to maximize knowledge gain. All studies
had a similar procedure design, but with different
frequencies of requested self-assessments per day and
different influencing factors and corresponding
scales. While our procedure design could function as
a blueprint for future studies, the things we changed
from study to study are the key elements to adjust for
each new context, in which a diary study based on our
design shall be conducted. The key concept and
influencing factors depend on the objective of the
research and on the demands of the studied domain or
organization. Furthermore, an essential lesson learnt
from conducting our studies is that feedback on
influencing factors is relevant mainly, if the assessed
factors are actionable in terms of a possibility for the
participants to change the manifestation of the factor.
Thus, we shifted the items assessed in the last study
more to behavioral strategies. From a research point
of view, the frequencies of assessments should be as
high as possible to collect a large data set for
subsequent analyses. However, in practice the
frequencies of self-assessments and number of items
used for assessment strongly rely on the feasibility in
terms of the time needed by the participants to answer
the surveys. This is especially true in case of
assessments during the work day as in our study
designs. In the following, the commonalities of the
study designs are described first and then illustrated
by the exemplary procedure of our latest study. Each
design consisted of:
An initial questionnaire for contact and
demographic data
Individual survey days including only work
A number of surveys per day with a designated
e-mail reminder
A short energy-related measurement at each
measurement point for the momentary state
Scales for retrospective assessments of different
influencing factors, e.g. sleep quality, work
characteristics, recovery activities, and used
work strategies
Feedback generated from the individual data
Two of our three studies included ten survey days
with up to four surveys per day at meaningful time
points for work and leisure in the morning, noon,
afternoon, and evening. In one study considering an
average working day with eight hours, there were
even up to eight surveys a day to complete, but just
for three days of participation then and with the same
questionnaire for all diaries. In the last studies, a final
questionnaire asked for the participants’ perception of
the study and generated feedback. The rest of this
section describes the so-called formr run (cf.
Section 2.3) of our latest study in order to illustrate
with a concrete example, how the procedure of further
studies can look like. The procedure is as follows:
When entering the study link, the questionnaire
shown first is for meta data like the email address for
further invitations, the favored starting day, typical
start time of the working day, and some demographic
data. Furthermore, the participants are asked to
estimate how their energy might develop throughout
a typical day. For this, we used a pictorial scale of
human energy (Lambusch et al., 2020; Weigelt et al.,
2022) as shown in Figure 2, because it is more natural
estimating a status with just one visual item.
The main study starts with an invitation link to the
first diary entry after a waiting time that lasts until the
chosen starting day one hour after the participant’s
individual work begin. Every diary contained a short
energy-related measurement comprising the pictorial
scale of human energy and a few items of verbal
Human Energy Diary Studies with Personalized Feedback: A Proof of Concept with formr
Figure 2: Pictorial scale of human energy with seven
response options ranging from a depleted to a fully-charged
battery according to Weigelt et al., 2022.
As energetic activation represents the subjective
experience of human energy, it includes all facets of
experiencing the presence or absence of energy, e.g.
vitality or zest, fatigue or exhaustion. With the
diversity of focal aspects of the phenomenon, there
are many common instruments that can be used to
measure sub-concepts of human energy in terms of
energetic activation. In order to keep the diaries as
short as possible, we had to decide for few focal
aspects to measure. We chose to use three items of the
vigor-subscale of POMS (Albani et al., 2005). In
earlier studies we used Ryan and Frederick’s
subjective vitality scale as adapted by Schmitt et al.
(Schmitt et al., 2017). Furthermore, we used three
items of the tension-subscale of POMS (Wyrwich and
Yu, 2011) for every diary in this study. The morning
diary, to which the mentioned invitation link leads,
complements the energy measurement with questions
about sleep, including e.g. the Insomnia Severity
Index (Bastien, 2001) and the day so far, e.g. morning
reattachment (Sonnentag and Kühnel, 2016), and
items for planning and goal setting of the German
version of the revised self-leadership questionnaire
(Andreßen and Konradt, 2007). The run waits 90
minutes for the participant to click the invitation link
and complete this diary entry and skips it in case the
participant doesn’t click the link. In any case the next
module is to wait until the individually chosen lunch
time, where the next invitation email with a link to the
noon diary is sent. In the noon diary questions about
e.g. job crafting (Lopper et al., 2020) complement the
energy measurement. As this entry shall be completed
after lunch and the invitation is sent at the given lunch
time, we wait a bit longer here for the participant to
complete the diary entry, namely 120 minutes, before
this diary is skipped. The afternoon diary is always
sent at 4 pm with a waiting time of 90 minutes and the
evening diary at 7 pm with a waiting time until 11:59
pm before the entry is skipped. In the afternoon
questions are posed about e.g. autonomy (Stegmann
et al., 2010), elective selection (Schmitt et al., 2012)
and micro-breaks (Kim et al., 2018). while in the
evening we ask for concepts like work-life-balance
(Syrek et al., 2011) and progress through
supplemental work (Weigelt and Syrek, 2017).
The described daily procedure starting with a
morning diary and ending with an evening diary is a
loop repeated over the course of the study. However,
invitations for diary entries are only sent on
workdays, not on weekends. Thus, on weekends a
waiting time takes effect. After five days of diary
entries, the participants of the second group get their
intermediate feedback after completing or skipping
the evening entry. After ten days of diary entries, all
participants get final feedback. After a pause, an
invitation to a closing survey is sent to the participants
to ask for perception of the study and generated
feedback. After completing this survey, the
participants have again access to their final feedback
via the link. Explanations and exemplary excerpts of
the generated feedback are given in the next section
on feedback development.
The feedback generated in our studies is intended to
empower employees to better understand their energy
levels and improve their energy self-management. In
this way, we strive to enable overload prevention and
promote lasting work pleasure. Instead of providing
just general information and tips, the feedback is
created personalized from the individual data, e.g.
showing a selection of only those influencing factors
most relevant for the specific person. The diary study
feedback can be seen as a step towards a
comprehensive tool helping people to identify those
factors, which have a major influence on their
individual energy level. To date, our study results
already indicate how highly individual energy curves
and factors are, supporting our endeavor and the
necessity for individual feedback complementing
rather general recommendations on energy self-
When designing the feedback, we decided that the
it should at least include graphs visualizing the
development of the participant’s energy and
representations of how the different scales correlate
with it. In our latest feedback design, we additionally
provide information on the development of the
person’s tension as well as on the manifestations of
the assessed influencing factors in the everyday work
life of the participant. Researchers should carefully
elaborate how to visualize which data in advance to a
run. For instance, visualizing a user’s level of human
energy over the time of a day in a line graph seems
more suitable than showing its portions in a radar
chart. Oppositely, visualizing the manifestations of a
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user’s different working characteristics in
comparison seems to be more reasonable with a radar
chart than with a line graph (cf. e.g. Chapter 6.3 in
(Skiena, 2017) on chart types). As it is very important
to enable the participants to understand what the
feedback means, descriptive texts should explain the
feedback data and limitations in interpretation. The
generated feedback actually addresses critical data in
the sense of Zannella et al. (Zannella et al., 2020).
Thus, its presentation was carefully elaborated in
collaboration with psychologists and cautionary notes
were included, e.g. for the influencing factors
regarding the difference between correlations and
causality. In order to visualize the development of the
participant’s energy level, two time contexts are
important according to existing research: 1) the day
level (Golder and Macy, 2011) and 2) the week level
(Weigelt et al., 2021). Thus, we provide the
participants with a diagram for both levels. For the
day level the participants were requested to estimate
their mean energy throughout a typical work day with
the pictorial scale. In the feedback, we show them
their estimation together with their actual mean
energy curve over a day (cf. Figure 3). For the week
level, we provide the participants a graph with their
mean vigor (as one of the manifestations of human
energy) of each day during the whole study period (cf.
Figure 4). The figures shown in this section are the
graphs generated by formr, only texts in the figures
are changed in sizes and have been translated from
German. A similar curve as in Figure 4 is shown for
the participant’s tension over the diary study period.
Furthermore, we provide information on the daytime
with the minimum and maximum mean values for
energy and tension, e.g. maximum tension was in the
morning with a mean value of 2,2. Next, a series of
radar charts illustrates how strongly the possible
influencing factors assessed are pronounced in the
participant’s everyday working life (see Figure 5).
The last diagram of the provided feedback
represents a core element for energy self-
management, namely the four strongest correlations
of the influencing factors with the participant’s vigor
(cf. Figure 6). In case the participants are interested in
reviewing the course of the four strongest correlating
factors over the study period in comparison to their
energy, it would be possible to create for future
studies a graph similar to Figure 4, complementing it
with four other line graphs for the correlating factors.
Figure 3: Exemplary formr feedback diagram of a
participant’s estimated (orange) and actual (blue) mean
energy (1 to 7) over the course of a day.
Figure 4: Exemplary formr feedback diagram of individual
vigor (1 to 5) over the course of the study. The black graph
shows the connected measurement values, whereas the blue
graph represents a smoothed curve with an enclosing grey
area highlighting the general trend.
Figure 5: Exemplary formr radar chart for characteristics of
a participant’s typical day assessed in the noon. It shows the
mean assessment value for the factors across all days of
Human Energy Diary Studies with Personalized Feedback: A Proof of Concept with formr
Figure 6: Exemplary formr bar chart displaying the four
strongest correlation coefficients of the personal vigor to
possible influencing factors.
We conducted a series of diary studies that
implemented the analyses and feedback we described
on a conceptual level in the previous chapter.
Participants were recruited using a convenience
sampling strategy, i.e. the invitation was spread
through word-of-mouth recommendation and social
media (e.g. posted on the platform Xing in a forum
about self-management and self-coaching). In sum,
74 persons participated in the studies. In the
following, we report on our insights during the studies
regarding feedback generation, participants’
perceptions and IT-support.
5.1 Observations from Data Analysis
Our study results show how much energy curves and
high correlating factors differ on an individual level,
which supports the need for personalized feedback in
addition to more general recommendations for energy
self-management. We illustrate the differences of
participants in daily energy in Figure 7. Also for the
high correlating factors, we observed that these are
largely different between subjects. An explanation for
this could be that participants differ in terms of e.g.
personality, cognition and also their working
conditions. An example for the latter is that postpone
or delegation behaviors are not possible if work-
related autonomy is rather small.
Furthermore, we were able to derive interesting
insights by analyzing the collected data. Since the
studies varied slightly and due to space limitations,
we are not able to report on all of our findings. A
sample finding is e.g. that negative correlations were
found for time spent in meetings and subjective
vitality. In regard to the factors influencing human
energy, it was e.g. discovered that strength use is
positively correlated with vitality. From this it can be
deduced tasks should be favored where personal
strengths can be applied and that time spent in
meetings should be reduced.
5.2 Preliminary Insights on How the
Participants Perceived the Study
In the studies we conducted, we collected both
qualitative and quantitative feedback from our
participants which we summarize below.
Comments to the applied scales. In general, we
did not receive negative feedback regarding the
understandability (with rare exceptions). However,
some new items were suggested by the participants
such as work tasks that were assigned at short notice
in the evening through mail, SMS or even phone calls
and that cause sleep problems or doing sports. A point
of criticism was that inapplicable questions could not
be omitted.
Feedback to the study execution. Concerning the
general study, there were only criticisms about the
procedure of the study. According to this, the study
should provide more flexibility, i.e. participants
wished to determine the time of the questionnaires
being sent and to limit questions to a subset they find
applicable for their daily life. Also, integration with
task calendar, e.g. in Outlook, was suggested in order
to not to miss questionnaires. Another idea was to
send funny and therefore encouraging messages to the
participants during the study in order to avoid the
“stiff” character of the questionnaires over time.
Furthermore, the issue of time-lag effects was raised,
e.g. to measure whether or not there is a drop in
performance after overproductive days.
General comments on the impact on personal life.
For many of the participants, these questionnaires
seemed to have a positive impact on their thoughts. In
some cases, it was reported that it stimulated
reflection and helped to gain insights into everyday
work and how different aspects affect work. In this
sense, the studies were able to provide “food for
thought”. Of course, some more critical remarks
occurred too. Predominantly, these were about
questions that were felt to be repetitive or irrelevant.
Also, the “one-off” nature of the feedback was
criticized, i.e. a more incremental feedback was
Perceived relevance and usefulness of the
feedback. We included a final questionnaire at the end
of the last two studies to ask for participants’
perceptions of the feedback. In one of the studies,
Scale-IT-up 2022 - Workshop on Scaling-Up Health-IT
Figure 7: Comparison of energy curves of different participants on the day level. Orange lines represent the participants
estimated energy curve and blue lines the actual energy.
participants (n=27) had to specify their agreement on
a 5 point Likert scale ranging from disagreement (1)
to complete agreement (5). In regard to the
proposition that the feedback is useful for everyday
work, most of the participants answered with 3-4 with
approx. 42% for each value. In regard the assertion
that the time invested in the study is useful, approx.
68% of the participants highly or even completely
agreed to this (4-5). Moreover, more than 60% of the
participants answered with 3-5 regarding the question
of being able to integrate the content of the feedback
into their everyday work. Furthermore, being able to
derive personal benefit from the feedback of the study
was highly agreed (4) by approx. 37%. In regard
whether the participant’s knowledge could be
expanded in the long term with the help of the
feedback, this question was mostly answered with 2
(21.1%) and 3 (52.6%). Finally, in regard to the
statements that new knowledge could be generated by
the study and that something could be learned
through the study, most participants somewhat or
highly agreed (3-4). This is also consistent with the
overall average, as the most common response
options for the entire final questionnaire were 3 with
33.1% and 4 with 32.3%. In sum, over 60% of the
participants responded positively to the study
evaluation form.
5.3 Challenges and Learnings
regarding the IT-support
Regarding the technical implementation of the study,
the most important learning was that timing problems
should be handled with caution. There is a so-called
“expiry date”, which can be set in the settings of each
questionnaire. It determines how long a questionnaire
can be filled in. However, only when this period is
exceeded, the participant can receive the invitation
for the next questionnaire. The period between the
questionnaire in the morning and at noon, for
example, was set to 300 minutes first. However, this
did not take into account that often questionnaires do
not arrive on time at 7 a.m., but also sometimes later.
If this is the case, the “expiry date” overlaps with the
invitation time of the following questionnaire and an
error occurs where participants get stuck in the run
and do not receive any further invitations. The
problem could be solved by subtracting 10 minutes
Human Energy Diary Studies with Personalized Feedback: A Proof of Concept with formr
from the expiry date. Such timing problems may be
caused due to the computational load of the server
that is hosting the study. We currently explore this
issue further. Another logical error found was that
after entering the last questionnaire of a day, the
participants jumped via the rewind module to the
invitation on the next morning. However, this only
works if the respondent completes the questionnaire
on the same day. If this does not happen, the run skips
a day. This problem was also solved by implementing
an if-statement before the last questionnaire.
Today’s working world can be characterized by
increased flexibility and ever growing complexity of
products and services in highly dynamic markets.
This induces high workloads, constant time-pressure
as well as blurring borderlines between different life-
domains. For individuals as well as organizations, it
can be hard to keep pace. Hence, good self-
management capabilities in terms of controlling the
own behaviors in a long-term desirable way are of
vital importance for promoting productivity as well as
sustainable health management. In this direction, we
suggest to combine researching phenomena with the
generation of personalized feedback as an integral
part of a study. In order to do so, we design and
implement IT-supported diary studies that provide
comprehensive and personalized feedback. In the
paper at hand, our contribution is that we (i) identify
and describe characteristics of such studies together
with the corresponding infrastructure (Section 3),
(ii) provide examples and suggestions for individual
feedback generation (Section 4) and finally (iii)
provide preliminary insights based on several studies
we already implemented and executed (Section 5).
A limitation of our research is the still small
amount of participants in our studies (n=74). In
addition, all of our studies were centered on working
behaviors and attitudes and their influence on
psychological constructs measured by established
scales, most notably “human energy”. Hence,
generalizations to other study topics have to be made
with caution so far. However, our results are quite
promising since in all studies, we were able to collect
required data, analyze the data and provide
meaningful feedback, according to our participants.
For the future, we want to develop our assessment
study with feedback further into a more intervention-
oriented study. In the first step, we will do this by
using the feedback as an intervention itself. Thus,
while we collected initial perceptions of the
participants on the generated feedback so far, we plan
to culminate the optimized conceptual and technical
realization focused in this article with a larger study
examining specifically the psychological effects of
our diary study with feedback on the participants. As
a next step, it could be decided, if further
interventions might be interesting to add and
research. Such interventions might be intended to
support behavior change. For example, if a person is
regularly low in energy after a meeting, the system
could suggest recovery strategies like taking a short
walk after meetings, so that this might become a habit
little by little.
Our approach may also be helpful in the domain
of eCoaching. Since coaching activities often imply
to explore and experiment with different
interventions and study their effect over a period of
time, the impact of different interventions could be
tested. Furthermore, an essential part of coaching
activities often is to identify contingencies between
variables, e.g. to identify how the interplay of certain
behaviors affects clinical symptoms or perceived
outcomes on target variables. Due to the powerful
statistical data analysis capabilities of R, such
contingencies could be identified in an automated
way and included in the personal feedback. Hence, an
avenue of future research would be to develop our
design more in the direction of coaching activities.
In summary, there is much opportunity for further
exciting developments and we hope that our results
will inform and inspire future IT-supported diary
studies that include personalized feedback as an
integral part of the study.
We would like to thank everyone who helped
implementing the different studies and feedback
designs, namely Maria Dehne, Christoph Gibcke,
Christoph Rosenau, Anastasiia Karpachova, Konrad
Seidler, Niklas Götz, Tabea Bröring, Leo Rehm,
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