ArticlePDF Available

PEACH, a smartphone- and conversational agent-based coaching intervention for intentional personality change: Study protocol of a randomized, wait-list controlled trial

Authors:

Abstract and Figures

Background: This protocol describes a study that will test the effectiveness of a 10-week non-clinical psychological coaching intervention for intentional personality change using a smartphone application. The goal of the intervention is to coach individuals who are willing and motivated to change some aspects of their personality, i.e., the Big Five personality traits. The intervention is based on empirically derived general change mechanisms from psychotherapy process-outcome research. It uses the smartphone application PEACH (PErsonality coACH) to allow for a scalable assessment and tailored interventions in the everyday life of participants. A conversational agent will be used as a digital coach to support participants to achieve their personality change goals. The goal of the study is to examine the effectiveness of the intervention at post-test assessment and three-month follow-up. Methods/Design: A 2x2 factorial between-subject randomized, wait-list controlled trial with intensive longitudinal methods will be conducted to examine the effectiveness of the intervention. Participants will be randomized to one of four conditions. One experimental condition includes a conversational agent with high self-awareness to deliver the coaching program. The other experimental condition includes a conversational agent with low self-awareness. Two wait-list conditions refer to the same two experimental conditions, albeit with four weeks without intervention at the beginning of the study. The 10-week intervention includes different types of micro-interventions: (a) individualized implementation intentions, (b) psychoeducation, (c) behavioral activation tasks, (d) self-reflection, (e) resource activation, and (f) individualized progress feedback. Study participants will be at least 900 German-speaking adults (18 years and older) who install the PEACH application on their smartphones, give their informed consent, pass the screening assessment, take part in the pre-test assessment and are motivated to change or modify some aspects of their personality. Discussion: This is the first study testing the effectiveness of a smartphone- and conversational agent-based coaching intervention for intended personality change. Given that this novel intervention approach proves effective, it could be implemented in various non-clinical settings and could reach large numbers of people due to its low-threshold character and technical scalability.
Content may be subject to copyright.
S T U D Y P R O T O C O L Open Access
PEACH, a smartphone- and conversational
agent-based coaching intervention for
intentional personality change: study
protocol of a randomized, wait-list
controlled trial
Mirjam Stieger
1*
, Marcia Nißen
2
, Dominik Rüegger
3
, Tobias Kowatsch
4
, Christoph Flückiger
5
and Mathias Allemand
1
Abstract
Background: This protocol describes a study that will test the effectiveness of a 10-week non-clinical psychological
coaching intervention for intentional personality change using a smartphone application. The goal of the
intervention is to coach individuals who are willing and motivated to change some aspects of their personality, i.e.,
the Big Five personality traits. The intervention is based on empirically derived general change mechanisms from
psychotherapy process-outcome research. It uses the smartphone application PEACH (PErsonality coACH) to allow
for a scalable assessment and tailored interventions in the everyday life of participants. A conversational agent will
be used as a digital coach to support participants to achieve their personality change goals. The goal of the study
is to examine the effectiveness of the intervention at post-test assessment and three-month follow-up.
Methods/Design: A 2x2 factorial between-subject randomized, wait-list controlled trial with intensive longitudinal
methods will be conducted to examine the effectiveness of the intervention. Participants will be randomized to
one of four conditions. One experimental condition includes a conversational agent with high self-awareness to
deliver the coaching program. The other experimental condition includes a conversational agent with low self-
awareness. Two wait-list conditions refer to the same two experimental conditions, albeit with four weeks without
intervention at the beginning of the study. The 10-week intervention includes different types of micro-interventions:
(a) individualized implementation intentions, (b) psychoeducation, (c) behavioral activation tasks, (d) self-reflection,
(e) resource activation, and (f) individualized progress feedback. Study participants will be at least 900 German-
speaking adults (18 years and older) who install the PEACH application on their smartphones, give their informed
consent, pass the screening assessment, take part in the pre-test assessment and are motivated to change or
modify some aspects of their personality.
Discussion: This is the first study testing the effectiveness of a smartphone- and conversational agent-based
coaching intervention for intended personality change. Given that this novel intervention approach proves effective,
it could be implemented in various non-clinical settings and could reach large numbers of people due to its low-
threshold character and technical scalability.
Keywords: Intentional personality change, personality change intervention, coaching intervention, smartphone,
conversational agent
* Correspondence: m.stieger@psychologie.uzh.ch
1
Department of Psychology and URPP Dynamics of Healthy Aging, University
of Zurich, Andreasstrasse 15, 8050 Zürich, Switzerland
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Stieger et al. BMC Psychology (2018) 6:43
https://doi.org/10.1186/s40359-018-0257-9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Background
There is a recent debate in personality science whether
and how personality traits can be intentionally modified
or changed over short periods of time by intervention ef-
forts. Although available research suggests that most
people want to change or modify some aspects of their
personality [13], psychological interventions for
intentional personality change are almost lacking. Only a
few studies have examined intentional personality
change over shorter periods of time [46]. These very
few existing studies are promising and suggest that
intended trait change in a desired direction is possible.
However, it is still an open question whether personality
change can be maintained or rather reflects temporary
changes that revert over time. This protocol describes a
study that will test the effectiveness of a non-clinical
psychological coaching intervention for intentional per-
sonality change that focuses on the Big Five personality
traits, i.e., neuroticism, extraversion, openness to experi-
ence, agreeableness, and conscientiousness.
Conceptual Framework of the Intervention
Since intervention efforts for intended personality change
are in their infancy, conceptual frameworks are needed to
develop theory-driven intervention programs. One ap-
proach would be to carefully develop specified treatments/
treatment guidelines for changing particular personality
traits. The other approach would be to develop interven-
tions based on more general (common) intervention princi-
ples [7]. The present coaching intervention is based on a
general (common) change mechanisms intervention frame-
work. General change mechanisms are assumed to be re-
sponsible for intermediate changes in someones
characteristics, skills, experiences, and behaviors, and even-
tually lead to improvements in the ultimate outcome or tar-
geted goal of an intervention. Allemand and Flückiger [7]
argue that four empirically derived general change mec-
hanisms from psychotherapy process-outcome research
[811] provide useful heuristic principles for intentional
personality change interventions and help to maximize the
effectiveness of intervention efforts. The four mechanisms
are: (1) actuating discrepancy awareness, (2) targeting
thoughts and feelings (insight), (3) targeting behaviors
(practice), and (4) activating strengths and resources. These
mechanisms highlight different perspectives of the immedi-
ate individual psychological outcomes and are highly con-
nected with each other [12]. In order to target those
general change mechanisms and to promote the change
process, the coaching intervention includes several
micro-interventions. Micro-interventions (specific tools
and techniques) are small interventions that are essential in
helping individuals to modify or change trait-related experi-
ences and behaviors in concrete real-life situations and help
to maintain the change process [13].
Actuating discrepancy awareness
The first change mechanism focuses on the awareness of
differences between the actual and the desired personal-
ity, which might facilitate the change process. The idea
is that personality traits can be most effectively targeted
and altered while people explore potential gaps between
their actual and desired personality (cf. [14]). Examples
of micro-interventions that target this change mechan-
ism are (a) the motivational interviewing approach, (b)
miracle questions, and (c) individualized progress feed-
back. The motivational interviewing approach [15]
serves to counterbalance advantages and disadvantages
of change and might eventually enhance individual
change motivation. By writing down pros and cons of
the actual and desired behavior and experience, people
can evaluate the gap between their actual and desired
personality. Miracle questions help people to think about
their future goals and their desired personality and thus
actuate discrepancy awareness between the actual and
the desired personality. Miracle questions are basically
thought experiments, which ask people to imagine their
desired future and personality [16]. Individually tailored
progress feedback is one of the most commonly used
change techniques in smartphone-based health interven-
tions [13] that helps people to focus on their discrepancy
awareness.
Targeting thoughts and feelings to realize insight
The second mechanism emphasizes reflective processes,
which may promote the personality change process by
helping individuals to reflect their thoughts, feelings, and
behaviors in a more systematic way. The following five
micro-interventions are known to be effective to activate
this change mechanism: (a) systematic reflection, (b) psy-
choeducation, (c) observational learning, (d) introspection,
and (e) identification of situational/contextual triggers.
Systematic reflection is a micro-intervention that helps
people to learn from experiences including failures and
successes [17]. Changing aspects of ones personality is
hard and is related to experiences of failures. Systematic
reflection helps to focus on the goal rather than on emo-
tional reactions after a failed task. To promote the change
process, it is also important to understand own beliefs and
expectations. Since people may have different self-theories
about the changeability of different aspects of personality
[18], fostering the knowledge transfer about personality
change in the form of psychoeducation may further pro-
mote the change process. Psychoeducation is a prominent
tool in cognitive behavioral therapy [19]. Other
micro-interventions, which also target thoughts and feel-
ings, are the observation and modeling of othersbehav-
iors (observational learning)[20,21], watching onesown
behaviors, thoughts, and feelings (introspection)[21,22],
and identifying situational and contextual triggers (e.g.,
Stieger et al. BMC Psychology (2018) 6:43 Page 2 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
people, society, surroundings; [23]). Being aware of situ-
ational and contextual triggers, which are connected to
the desired or actual behavior, can actually help to show
desired behaviors more often and to avoid actual behav-
iors [23].
Targeting behaviors to realize practice
This mechanism focuses on learning and reinforcing
new behaviors and skills, such as compensatory or cop-
ing skills, and to behave in new roles. To achieve change
goals, individuals should gradually increase engagement
in activities and new behaviors connected to their
change goals. Two micro-interventions are included in
the intervention to target this change mechanism: (a)
implementation intentions and (b) behavioral activation.
Generating implementation intentions in the form of
specific if-then planscan lead to better goal attainment
and help individuals in habit formation [24]. This
micro-intervention was successfully used in previous
intervention work for intentional personality change [5].
Behavioral activation tasks help individuals to perform
novel behaviors and activities. Behavioral activation is
based on principles of reinforcement and learning theory
and was originally developed for the treatment of de-
pression [25]. Magidson and colleagues[26] suggest this
micro-intervention also for intentional personality inter-
ventions and used it in their case study.
Activate strengths and resources to realize strengths-
orientation
This change mechanism capitalizes on individual and
interpersonal strengths and resources. Resources might
be related to personal skills and capabilities, motivational
readiness and preparedness for change, as well as social
support. Micro-interventions identified to target this
mechanism include (a) organizing a change team, (b)
keeping a diary of strengths and resources, (c) using the
tree of resources, and (d) thinking about future plans,
dreams and hopes. An informed change team, including
significant others such as friends and family members
can provide social support throughout an intervention
and help people to attain their change goals [27]. Keep-
ing a diary of strengths and resources [28] or to write
down individual resources inside the tree of resources
[8,29] can further promote the change process by
reflecting about personal strengths and positive aspects of
life. Another micro-intervention activates individualsre-
sources and enhances change motivation by thinking about
future plans, dreams and hopes by getting asked questions
derived from the life story interview approach [30].
Smartphone-Based Coaching Interventions
Smartphones provide a powerful tool set for psycho-
logical and behavioral micro-interventions for several
reasons [3138]. First, smartphones are ubiquitous with
increasingly powerful technical abilities and make so-
phisticated micro-interventions appealing and widely ap-
plicable. Second, unlike desktop computers, laptops or
tablets, smartphones are nearly always with the person.
Third, people often have a positive emotional attach-
ment to and daily routines in dealing with their smart-
phones, which can reduce the barriers to adoption and
increase acceptance of micro-interventions. Fourth, the
combination of powerful technical abilities of smart-
phones and their proximity to their owners offers the
ability to detect useful context information that can be
used to individualize interventions. Moreover, context
awareness features enabled through sensing and
phone-based personal information allows creating
just-in-time micro-interventions that provide users with
support at times when that support is most needed. Fi-
nally, interventions using smartphones are scalable,
cost-effective, low-threshold, applicable to a wide variety
of participants and show promising retention rates. For
example, a recent study in the public health context
found that owning a smartphone was not a major bar-
rier to study participation for most respondents [...] in-
cluding those who were unemployed, i.e. with a low
socio economic status [39]. In another recent study, re-
tention rates of smartphone-based interventions are
promising as participants had eight conversational turns
with a smartphone-based chatbot per day on average
over the course of six months [40,41].
The talk-and-tools-paradigm
Smartphone interfaces also enable the application of the
so-called talk-and-tools paradigm [42,43]. That is, smart-
phones are able to offer scalable communication features
with the help of conversational agents (the Tal k , e.g., for
motivational interviewing purposes), i.e., computer pro-
grams that imitate a conversation with a human being
[4447]. In contrast to popular voice-based conversational
agents such as AmazonsAlexa,Apples Siri, text-based
conversational agents (often called chatbots) are so far
less prominent. Promising examples include Florence (get-
florence.co.uk), Lark (web.lark.com)orWoebot(woebo-
t.io). In contrast, interfaces of smartphones can also be
used to deliver a broad range of Tools, i.e., the building
blocks of micro-interventions (e.g., keeping a diary of re-
sources, a reminder for individual implementation inten-
tions or the delivery of psychoeducation video clips). The
application of this talk-and-tools paradigm can not only
complement and extend existing face-to-face counseling
sessions to the everyday life of individuals, but it can also
provide new means to offer smartphone-based coaching
interventions in a scalable fashion where a personal
coaching approach is not feasible due to limited reach,
personnel or budget.
Stieger et al. BMC Psychology (2018) 6:43 Page 3 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Design of conversational agents
Due to limited evidence on effective designs of text-based
conversational agents on mobile devices [48,49], it is es-
sential to study design features of conversational agents
and how they help individuals to reach their goals. Con-
versational agents are designed to interact with a human
like a human. The Computers as Social Actors theorem
by Reeves and Nass [50] confirms that individuals apply
social behaviors and heuristics typical for social interac-
tions with other human beings to interactions with com-
puters and conversational agents.
Research in the field of counselling psychology and
psychotherapy has shown that working alliance, a collab-
orative quality and the degree to which health profes-
sionals and patients engage with each other, is associated
with the therapeutic process and robustly linked to treat-
ment success in face-to-face therapy as well as in online
therapy (r= .28; [51]) [52,53]. The concept of working
alliance can be adapted to the relationshipbetween in-
dividuals and conversational agents and their interac-
tions (e.g., quality and length of messages exchanged or
frequency of interactions). It can be expected, that when
a conversational agent takes over the role of a communi-
cation partner and embodies a digital coach, its commu-
nication style and role will affect relationship-building
processes and, in part, treatment success (e.g., [54,55]).
Hence, it can be assumed that the choice of specific
verbal cues will increase an individuals working alliance
with a conversational agent. The present conversational
agent-based intervention will focus on one specific ver-
bal cue, namely whether the chatbot can refer to itself
using the first-person pronoun I. The use of Iauto-
matically implies a sense of human self-awareness or
self-concept by the chatbot [56], making it more an-
thropomorphic and relatable, than a conversational
agent without a self-concept.
In order to test the effects of a self-aware versus a
non-self-aware conversational agent on working alliance
and intervention effectiveness, two conversational agents
will be experimentally manipulated, such that a self-a-
wareconversational agent will present itself as a tangible
and present entity by actively referring to itself (May I
help you?) in contrast to an impersonal control conversa-
tional agent which will refrain from referring to itself (Do
you need help?) and remains less tangible as an entity,
fading the anthropomorphic identity of the conversational
agent into the background. The overall conversational
streams, message lengths, coaching elements, and sched-
ule will be kept the same in both conditions.
Research Goals and Hypotheses
The first goal of the present study is to examine the ef-
fectiveness of PEACH, a smartphone- and conversa-
tional agent-based coaching intervention for intentional
personality change. The outcome research hypothesis is
that two experimental conditions (high versus low
self-aware conversational agent) will be more effective
with respect to personality trait change in comparison to
the two waiting list conditions. Furthermore, based on
previous work on the effects of anthropomorphized
computer-mediated communication on human behavior
[57], the differential outcome research hypothesis is that
the self-aware conversational agent will be more effective
in terms of relationship-building, promoting interven-
tion adherence and thus treatment success than the low
self-aware conversational agent.
The second goal is to explore underlying processes and
mechanisms that improve the outcomes of the interven-
tion. Two approaches are used for process assessments:
self-reports and smartphone sensing. Both methods in-
clude an intensive longitudinal design. This allows explor-
ing associations between actively (self-reports) and
passively (sensors) assessed intervention processes.
Methods/Design
Design
In this study protocol, we describe a 2x2 factorial
between-subject randomized, wait-list controlled trial with
intensive longitudinal methods studying the effectiveness
of a 10-week smartphone- and conversational agent-based
coaching intervention for intentional personality change.
The effectiveness of the intervention will be compared
across two dimensions: intervention (experimental versus
wait-list control) and conversational agent design (high
versus low self-awareness). Participants will be randomly
assigned to one of four conditions: (a) experimental condi-
tion 1: conversational agent with high self-awareness, (b)
experimental condition 2: conversational agent with low
self-awareness, (c) wait-list condition 1: conversational
agent with high self-awareness, (d) wait-list condition 2:
conversational agent with low self-awareness. Participants
in the wait-list control conditions will receive no interven-
tion for the first four weeks to document the natural
course of their personality change without expecting inter-
ventional effects. To monitor progress, the wait-list con-
trol groups will respond to the same weekly
questionnaires during those four weeks as the subjects
from the experimental conditions. Additionally, they are
passively tracked by smartphone sensors. After the four
weeks without any intervention, subjects of the wait-list
control conditions will receive the same intervention as
subjects of the experimental conditions - depending on
their conversational agent embedding high or low
self-awareness cues.
Participants and Recruitment
The targeted sample will include at least 900
German-speaking adults, who install the PEACH App
Stieger et al. BMC Psychology (2018) 6:43 Page 4 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
on their smartphones, give informed consent, pass the
screening, fill in the pre-test assessment and start with
the intervention. To assure an adequate power to detect
statistical significance and to demonstrate a small to
medium effect of a pre-post time by group interaction
we require data from 300 participants. Assuming an α
error level of 0.05, a statistical power (1-β) of 0.80, and a
correlation of 0.40 between the pre- and
post-measurements and 75 completers for each group,
we would be able to detect a small effect of Cohensd=
.22. Computing power for repeated measures, which is
the case in this study, is more complex. As such, this
power analysis only gives a rough idea of the effect sizes
the study could reasonably detect. In a similar study
[33], 67% of participants completed the post-test survey
after 6 weeks, from a cohort of 273 who started the
intervention. Based on this estimate and taking the lon-
ger duration of this study into account, we expect even
more attrition. Should drop-out rates be higher than ex-
pected, we may recruit additional participants to ensure
sufficient statistical power. To be eligible for the study,
participants must be: (1) 18 years or older; (2) able to
read German; (3) not in a psychotherapeutic or psychi-
atric treatment; (4) owner of a smartphone (Android or
iOS) with mobile internet connection; and (5) interested
and motivated to participate at the intervention and to
change some aspects of their personality. The focus of
this intervention study is explicitly on healthy adults.
Thus, adults with mental health disorders and other psy-
chosocial problems will be excluded. Participants will
complete an online eligibility screening that checks for
the inclusion criteria. Excluded candidates with mental
health disorders and psychosocial problems will be pro-
vided with an information and contact details of the psy-
chological counseling service of the University of Zurich.
We will primarily use university mailings and social
media advertisements for the recruitment process. Add-
itionally, potential participants will respond to flyers or
word-of-mouth recruitment. Interested people will be
directed to either the website of the project (www.perso-
nalitycoach.ch) or to the Apple Store/Google Play Store
to receive detailed information about the study aims, in-
terventions, assessments, reimbursement, and data pro-
tection and download the mobile application.
Participants will be automatically and randomly assigned
to one of four conditions (Fig. 1). In total, the two ex-
perimental conditions will be oversampled and will in-
clude 2/3 and the control condition 1/3 of all
participants (full randomization in all four conditions).
The automated allocation and randomization procedures
will be computer generated. In this way, we aim to en-
sure that the conditions are fully randomized with re-
spect to the participantsbaseline characteristics
(allocation concealment). Because all participants will be
treated using a comparable coaching intervention, par-
ticipants are blinded to the two conversational agents.
Spill-over effects could occur since participants might
know each other and talk about the procedure of the
intervention. After obtaining informed consent and
passing the screening assessment, participants will be di-
rected to the pre-test assessment. The procedure and de-
sign of the study are also depicted in Fig. 1.
Reimbursement of 25 Swiss Francs for taking part in
the pre-test and follow-up assessment will be offered to
study participants. Consistent with prior work (e.g. [58]),
participants will be able to earn credits for active partici-
pation and by fulfilling specific tasks during the inter-
vention such as engaging with the conversational agent
(maximum 8 credits per day), experience sampling mea-
surements (3 credits per measurement occasion), weekly
assessments (20 credits per assessment), and photo up-
loads (15 credits per upload). Participants can collect
1,000 credits in total and reach bronze status with 250
credits or more, silver status with 500 credits or more,
and gold status with 750 credits or more. According to
their status, participants earn tickets for the lottery
(bronze status = 1 ticket, silver status = 5 tickets, gold
status = 10 tickets). Participants can win 100 Swiss
francs, 200 Swiss francs, and 300 Swiss francs in cash.
Procedure
The procedure is shown in Fig. 1. After having com-
pleted the pre-test assessment, participants get instant
feedback on their actual Big Five personality trait profile
(BFI-2; [59]). This feedback should help participants to
choose their appropriate change goal. Participants have
to pick one change profile out of nine, which fits the
most to their individual change goal. Each of these nine
profiles explains normal characteristics of a person with
high or low levels in the corresponding Big Five person-
ality trait. To be more precise, participants can choose
between nine personality change profiles: (1) increase in
conscientiousness, (2) decrease in conscientiousness, (3)
increase in extraversion, (4) decrease in extraversion, (5)
increase in open-mindedness, (6) decrease in
open-mindedness, (7) increase in agreeableness, (8) de-
crease in agreeableness, and (9) decrease in negative
emotionality. For ethical reasons an intervention to in-
crease negative emotionality will not be offered. Partici-
pant then indicate the strength of their chosen change
goal on an 8-point scale from 0 = not at all to 7 =
totally and their willingness to change (i.e., goal commit-
ment and goal attainability; [60]). Additionally, partici-
pants are asked to share a link with at least three close
friends, family members and their intimate partner to
obtain an observer-report on the Big Five personality
traits (BFI-2-S; [61]) (Table 2).
Stieger et al. BMC Psychology (2018) 6:43 Page 5 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The first week of the study is an experience sampling
week to measure personality manifestations in daily life
(for more details, see below). The personality change
intervention then lasts over 10 weeks. For each of the 10
weeks, weekly core themes will be provided (Table 1).
Moreover, six different types of micro-interventions will
be used in the intervention (see below). All participants
are actively involved in two daily dialogues with the con-
versational agent. In the morning at an individually pre-
ferred time participants receive the first message for the
morning dialogue and in the evening again at an indi-
vidually preferred time participants receive the first mes-
sage for the evening dialogue. Participants have the
opportunity to read the dialogue until it is time for the
next dialogue. A conversational agent will be used to re-
mind participants to complete questionnaires, to guide
them through micro-interventions, to promote commit-
ment, to motivate participants, and to support the
change process (Fig. 2). During these conversations, a
combination of pre-defined answers and free-text input
is used to constrain the dialog along pre-defined coun-
selling paths and to give participants autonomy where
needed (e.g., for the definition of implementation inten-
tions in the if-then form). If participants do not actively
use the PEACH app over three days, the study team will
contact them via the Support-Team Channel(Fig. 2)
and ask them whether there occurred any problems or
whether they have any unanswered questions to promote
adherence. After the intervention, there is a second ex-
perience sampling week and then participants are asked
to answer the post-test assessment and the three-month
follow-up assessment (Fig. 1). Moreover, participants
were asked at post-test and follow-up assessment to
share a link with their close friends, family members or
intimate partners, who already provided their
observer-reports at pre-test assessment, to obtain a sec-
ond and third observer-report on the Big Five personal-
ity traits (BFI-S-S; [61]).
Weekly Core Themes and Micro-Interventions
The structure of the PEACH intervention includes (a)
weekly core themes with specific micro-interventions
and (b) micro-interventions that are not directly related
to the weekly core themes. The weekly core themes and
the micro-interventions that were used every day for 10
weeks are shown in Table 1. In the following, we briefly
discuss the six types of micro-interventions that were
used in PEACH: (1) individualized implementation
Fig. 1 Study design
Stieger et al. BMC Psychology (2018) 6:43 Page 6 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 1 Schedule of weekly core themes and micro-interventions
Week Weekly core
theme (Source)
Brief description Individualized
implementation
intention
c
Psycho-education
b
Behavioral
activation
tasks
c
Individualized
progress
feedback
a
1 Organizing a
change team
d
[27]
Participants are asked to inform 1-3 sig-
nificant others such as friends or family
members to talk with them about their
change goals, the coaching intervention
itself and to keep them updated during
the intervention.
Implementation
intention 1
Daily film clip or
scientific input
Behavioral
activation
task 1
Dashboard
2 Learning from
experiences by
systematic
reflection
b
[17]
People are asked to analyze their own
behavior and advance explanations for
the resulting success or failure to learn
from both. Questions that prompt self-
explanations include: How did you con-
tribute to the performance?or How ef-
fective were you in the experience. Then
participants are confronted with questions
such as Consider a different approach
that could have been taken.And finally
they should ask themselves: What
worked and what did not work? How will
you behave in the future?
Implementation
intention 2
Daily film clip or
scientific input
Behavioral
activation
task 2
Dashboard
3 Identifying
situational/
contextual
triggers
b
[23]
Participants learn how to identify
situational and contextual triggers (e.g.,
people, places, time in the day) that help
or hinder them to show their desired
behavior.
Implementation
intention 3
Daily film clip or
scientific input
Behavioral
activation
task 3
Dashboard
4 Thinking and
writing about
the pros and
consof
change
a
[15]
Participants think about advantages and
disadvantages of changing in the desired
direction and of staying the same. This
might eventually also enhance individual
change motivation.
Implementation
intention 4
Daily film clip or
scientific input
Behavioral
activation
task 4
Dashboard
5 Learning from
others by
observational
learning
b
[20,21]
Participants should look out for people in
their environment, who already show
their desired behavior. They analyze what
these people are doing differently and try
to model this behavior.
Implementation
intention 5
Daily film clip or
scientific input
Behavioral
activation
task 5
Dashboard
6 Self-reflection
by means of
introspection
b
[21,22]
Participants should watch their own
thoughts and feelings when they are able
to show their desired behavior and
thoughts and feelings when they are not
able to show the desired behavior.
Implementation
intention 6
Daily film clip or
scientific input
Behavioral
activation
task 6
Dashboard
7 Keeping a diary
of strengths
and resources
d
[28]
Participants are asked to think about what
they are grateful in life and about their
personal strengths.
Implementation
intention 7
Daily film clip or
scientific input
Behavioral
activation
task 7
Dashboard
8 Reflecting
about strengths
and resources
using the tree
of resources
d
[29]
Participants write down individual
resources inside their tree of resources in
order to visualize and reflect about
personal strengths and positive aspects
of life.
Implementation
intention 8
Daily film clip or
scientific input
Behavioral
activation
task 8
Dashboard
9 Thinking about
the desired
personality
using miracle
questions
a
[16]
Miracle questions are thought
experiments, which ask people to
imagine their desired personality, their
desired future and specific plans and their
priorities for the
next five years.
Implementation
intention 9
Daily film clip or
scientific input
Behavioral
activation
task 9
Dashboard
10 Looking
forward and
thinking about
the future
d
[30]
Participants should think about
future plans, dreams, hopes,
and poss
Implementation
intention 10
Daily film clip or
scientific input
Behavioral
activation
task 10
Dashboard
Note.
a
Actuating discrepancy awareness;
b
targeting thoughts and feelings to realize insight;
c
targeting behaviors to realize practice;
d
activate
strengths and resources to realize strengths-orientation; since these general change mechanisms are overlapping in content, weekly core themes and
micro-interventions might fit to more than just one general change mechanism
Stieger et al. BMC Psychology (2018) 6:43 Page 7 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
intention, (2) psychoeducation, (3) behavioral activation, (4)
self-reflection, (5) resource activation, and (6) individualized
progress feedback. The included micro-interventions were
selected to target and to activate the general (common)
change mechanisms in order to maximize the effects of the
intervention [7].
Individualized implementation intentions
An implementation intention is a self-regulatory strategy
in the form of an if-then planthat can lead to better goal
attainment [5,24]. This micro-intervention targets the
general change mechanism targeting behaviors to realize
practice. Participants generate one individual and specific
implementation intention based on suggested behavioral
activation task every Sunday. This individually built imple-
mentation intention should be implemented in daily life
during the following week as often as possible. Examples
for implementation intentions are: If I have to work con-
centrated, then I switch into flight mode(Productivity,
Conscientiousness), If I have no meetings before 1:00 p.
m,, then I will go to the gym.(Productivity, Conscien-
tiousness) or If I see something beautiful, then I will take
aphoto.(Aesthetic Sensitivity, Open-Mindedness).
Psychoeducation
Psychoeducation fosters knowledge transfer about person-
ality dispositions, personality change and its outcomes.
This micro-intervention operationalizes the general
change mechanism targeting thoughts and feelings to
realize insight. In the present coaching intervention, par-
ticipants receive every morning either a short film clip or
a message with scientific food for thought.Intotal,we
developed 36 film clips (11 film clips providing informa-
tion about personality dispositions and personality change
in general and 5 film clips for each participant fitting to
the chosen change goal and its outcomes) and 104 scien-
tific messages (34 providing input about personality dispo-
sitions and personality change in general and 10 messages
for each participant fitting to the chosen change goal).
Film clips provide worst- and best-case scenarios and sci-
entific facts about the advantages of achieving the desired
change. These interactive elements should also promote
motivation and adherence among participants.
Behavioral activation tasks
Behavioral activation directly changes actual behavior
and reinforces new behavior. This micro-intervention
Fig. 2 The PEACH App and its Components. Note. Chat-based interaction with the conversational agent PEACH (left), the sidebar (middle) that
allows participants to switch to either a dashboard with a personalized overview of the current status of the intervention (right), a media library
used for psychoeducational video clips, a chat channel that allows participants to communicate with the Support-Team, or a page for
frequently asked questions about the PEACH study and the app
Stieger et al. BMC Psychology (2018) 6:43 Page 8 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
operationalizes the general change mechanism targeting
behaviors to realize practice. In the present coaching
intervention, participants receive three new suggestions
of behavioral activation tasks every Sunday, which fit to
their chosen change goal [25,26]. Out of these three sug-
gestions, participants select one behavioral activation task
with the goal to implement the task in their daily routine
during the following week. Examples for behavioral activa-
tion tasks are: Dont procrastinate and do things right
away.(Productiveness, Conscientiousness), Tidy up a
part of your flat every day.(Organization, Conscientious-
ness) or Take a photo of something beautiful every day.
(Aesthetic Sensitivity, Open-Mindedness). In total, we de-
veloped 12 behavioral activation tasks for each of the nine
Big Five personality trait profiles (108 behavioral activa-
tion tasks in total) (cf. [59]).
Self-reflection
Self-reflection is a tool to exercise introspection, learn
from experiences including successes and failures. This
micro-intervention is included to target the general
change mechanism targeting thoughts and feelings to
realize insight. Different tools to exercise self-reflection
are included in the weekly core themes, which change
every week to enhance adherence and are embedded in
every dialogue in the evening (Table 1).
Resource activation
Resource activation capitalizes on individual and interper-
sonal strengths and resources.This micro-intervention is
included to target the general change mechanism activating
strengths and resources to realize strengths-orientation.
Tools including resource activation are also included in the
weekly core themes, which change every week (Table 1).
Individualized progress feedback
Individually tailored progress feedback is one of the most
commonly used change techniques in smartphone-based
health interventions [13] that helps people to focus on
their discrepancy awareness. This micro-intervention tar-
gets the general change mechanism actuating discrepancy
awareness. Participants constantly receive individualized
graphical feedback on the dashboard of the PEACH app
(Fig. 2). For instance, they can check whether they are
already approaching their change goal compared to the
beginning of the intervention. Additionally, they get feed-
back about how often they had opportunities to show
their weekly implementation intention and how often they
actually implemented it during the last seven days. Fur-
thermore, they can check their momentary status (bronze,
silver or gold status) and see the credits they have already
earned during the intervention (Fig. 2).
Assessment Strategy
The assessment strategy includes (1) a screening assessment
(self-reported), (2) an outcome assessment (self-reported
and observer-reported), (3) a process assessment (self-re-
ported), and (4) smartphone sensing. An overview is shown
in Table 2. These different types of assessments will be fur-
ther elaborated in the following.
Screening assessment
During the onboarding process (Fig. 1), participants will
respond to two screening questionnaires to check for eligi-
bility. Participants are directed from the PEACH app to
the online survey tool (limesurvey.org), so that they can
answer the screening questionnaires on their smartphone.
Short forms of the Symptom-Check List (SCL-K11; [62])
and Depression Scale (ADS-K; [63]) will be used to assess
mental health disorders and other psychosocial problems
(Table 2). Individuals with scores above the cut-off value
in the SCL-K11 (14) and above the cut-off value in the
ADS-K (19) will be excluded and are provided with in-
formation and contact details of the psychological coun-
seling service of the University of Zurich.
Outcome assessment
Self-reports The self-reports include a pre-test, a
post-test and a three-month follow-up assessment.
Pre-test assessment will take place before the intervention,
post-test assessment after the intervention and the
follow-up assessment three months after the end of the
intervention to check whether personality changes could
be maintained over a longer period of time or revert over
time. At all points of measurement participants will be
automatically directed from the PEACH app to the online
survey tool (limesurvey.org) to answer all questionnaires
on the smartphone (Fig. 3). The main outcome assessment
includes the Big Five Inventory 2 (BFI-2; [59]) to assess
the Big Five personality traits and trait-related facets. Fur-
ther outcome variables and control variables are willing-
ness to change [60], implicit theory of personality [64],
satisfaction with life (SWLS; [65]), satisfaction with life do-
mains [2], and self-esteem (RSES; [66]) (Table 2).
Main outcomes regarding the relationship building
process with the conversational agent include the follow-
ing: Working alliance will be assessed using an adapted
short-version [67] of the Working Alliance Inventory
(WAI-SR) [52] based on Kiluk et al.s[68] work, who
adapted the complete WAI to measure working alliance
with a technology-based intervention (WAI-Tech). To
further understand the perception of the conversational
agents, the Perception of Robots scale [69] and trust
measures [70] to assess trust development mechanisms
will be included. Interpersonal closeness will be mea-
sured with the Inclusion-of-the-Other-in-the-Self (IOS)
Stieger et al. BMC Psychology (2018) 6:43 Page 9 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 2 Measures
Intervention
Screening Pre-Test Experience
Sampling
Daily Weekly Post-Test Follow-up Evaluation
Screening
Symptom-Check List (SCL-K11; [62]) x
Depression Scale (ADS-K; [63]) x
Demographics x x
Main Outcome Assessment Self report
Big Five Personality Inventory (BFI-2; [59]) x x x
Main Outcome Assessment Observer Report
Big Five Personality Inventory (BFI-2-S; [61]) x x x
Process Assessment Self report
Big Five Personality Inventory 2 (BFI-2-S; [61]) x
Big Five personality states x x
Affect (PAM; [75]) x x
Information about current environment x
Stress level x
Realization of implementation intention x
Opportunities for realization of implementation intention x
Strength of change goal x x x x
Subjective perception of change x x x
Learning experience x
Inclusion-of-the-Other-in-the-Self [71] x biweekly x
Working Alliance Inventory (WAI-SR, [67]) x biweekly x
Perception of Robots [69]xx
Trust [70] x four-weekly x
Further Outcome & Control variables Observer report
Demographics x x x
Type and closeness of relationship x x x
Time spent with target person x x x
Further Outcome & Control variables Self report
Willingness to change [60]x
Implicit theory of personality [64]x xx
Satisfaction with life domains [2]x xx
Satisfaction with Life Scale [65]x xx
Rosenberg Self-Esteem Scale (RSES; [66]) x x x
Engagement in self reflection x x
Engagement in practice x x
Feedback on components of the coaching x
Technology acceptance scales [74]x x
Internet usersprivacy concerns [89]x
Technical anxiety [90]x
Manipulation check items x x
Stieger et al. BMC Psychology (2018) 6:43 Page 10 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
scale [71], an established and reliable instrument to meas-
ure perceived closeness of a relationship [72]. Manipula-
tion checks for perceived self-awareness of the
conversational agents [73] will be conducted every four
weeks to confirm that the manipulation had been scripted
thoroughly throughout the 10-weeks of intervention.
Overall satisfaction with the app, ease of use, perceived
enjoyment, and perceived usefulness will be measured
after the first week and at the end of the intervention
[74] to identify differences in the perception of the con-
versational agent due to differences in the usability of
the app. Moreover, we are interested in qualitative feed-
back of users at the beginning of the interaction with the
conversational agents (first impression) and at the end
of the intervention to eventually improve the interaction
with and perception of the conversational agents.
Observer reports In addition to self-reports,
observer-reports by close others will be assessed. At the
beginning of the study, participants will be asked to share
a link to the online observer-report questionnaires with at
least three close friends, family members or their intimate
partner. Observer-reports include the Big Five personality
traits (BFI-2-S; [61]), type and closeness of the relationship
and time spent with the target person. Observer-reports
will be assessed at pre-test, post-test and follow-up assess-
ment (Table 2).
Process assessment
Experience sampling with self-reports One week before
the intervention and one week after the intervention (Fig. 1),
there will be an experience sampling with self-reports in-
cluding four assessments per day (Fig. 3)atrandomtimes
once in each of four predefined time windows: 9:30 a. m. -
11:30 a. m., 12:30 p. m. - 14:30 p. m., 15:30 p. m. - 17:30 p.
m., 18:30 p. m. - 20:30 p. m. Participants will be asked to be-
have the first week as normal as possible and not to change
anything in their behavior in order to measure their baseline
behavioral signatures. After the intervention, participants
are asked to answer the same experience sampling questions
again during one week. This allows to check for changes in
the behavioral signatures as a result of the intervention. Ex-
perience sampling assessments include the photographic
Fig. 3 User interface for survey data collection. Note. Experience sampling assessment with self-reports (left) and daily diary assessment of the Big
Five personality states (right) using bipolar adjective items (ad hoc translation from German)
Stieger et al. BMC Psychology (2018) 6:43 Page 11 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
affect meter (PAM; [75]) to assess momentary affect, five
bipolar adjective items to assess the Big Five personality
states (presentation in a random order), a few questions
about the current location (e.g., indoors versus outdoors),
activity, and social environment (e.g. alone versus with other
people)atthemoment,andasingleitemtomeasure
momentary stress.
Daily diary and weekly self-report assessments Dur-
ing the intervention, there will be daily diary and weekly
measurements to assess individual change progress. The
photographic affect meter (PAM; [75]) and ten bipolar
adjective items to assess Big Five personality states will
be used on a daily basis every evening. Additionally, par-
ticipants are asked every evening whether they had op-
portunities to show their individual implementation
intention and whether they could perform their imple-
mentation intention. There will be a weekly assessment
every Sunday including a short version of the BFI-2
(BFI-2-S; [61]) (Table 2).
Smartphone sensing
Smartphone applications can get access to data from
sensors and usage logs (e.g. location, surrounding de-
vices via Bluetooth, logs of application usage and
phone calls), which allow objective measurement of
behavior, and inferences about userspersonality [76,
77]. Using this data, it may also be possible to detect
changes in personality over short periods of time. If
the application detects changes in behavior that are
consistent with desired changes in personality and as-
sociated with the use of the PEACH app, this would
constitute complementary evidence for PEACHs
effectiveness.
Technological Background of the Intervention
The smartphone-based coaching intervention is based on
the MobileCoach (www.mobile-coach.eu). The Mobile-
Coach is an open source platform for the design, delivery
and evaluation of scalable smartphone-based interventions
[40,43,78]. It is available via the research and
industry-friendly Apache 2 license and follows a
client-server model. The rule-based intervention logic and
messages are defined by intervention authors on the ser-
ver. MobileCoach then acts as a conversational agent and
uses these rules to send out the intervention messages to
client applications on mobile devices. MobileCoach also
allows to react to answers given by intervention partici-
pants and can deliver these interventions via the widely
available short message service (e.g., to lower the thresh-
old for participation and to maximize the reach of an
intervention) and/or via dedicated mobile messaging apps
for Apples iOS and Googles Android platform. The mo-
bile messaging apps allow not only to fully customize the
user experience to a particular target group (e.g., the look
and feel of the app with various conversational agents) but
also to use sensor data from smartphones and/or other
connected devices to deliver just in time adaptive inter-
ventions based on end usersstates of receptivity and vul-
nerability [79,80].
Previous studies have demonstrated the effectiveness
and reach of MobileCoach-based interventions with re-
gard to problem drinking in adolescents [58] and per-
ceived ease of use, enjoyment, therapy adherence and
scalability in a childhood obesity intervention [40,43].
In addition to delivering interventions, MobileCoach is
also used for the collection of intensive longitudinal data
in situ (e.g., for ecological momentary assessments), for
example in a clinical trial on stress disorders.
In the PEACH study, the iOS and Android apps of
MobileCoach are used to guide participants through the
micro-interventions, providing motivation, promoting
commitment, and reminding them to complete ques-
tionnaires. During these conversations, a combination of
pre-defined answers and free-text input is used to con-
strain the dialog along pre-defined counselling paths and
to give the participant autonomy where needed (e.g., for
the definition of implementation intentions in the
if-then form). With a swipe-to-the-right gesture or via a
menu button, participants can open the sidebar of the
PEACH app (Figure 2) from which they can navigate to
(a) their personal dashboard, (b) a media library used for
psychoeducation video clips which are unlocked along
the intervention path, (c) a second chat channel Sup-
port-Teamfor a traditional WhatsApp like communica-
tion with the study team (e.g., to clarify technical
questions and comments), or (d) to a page for frequently
asked questions about the PEACH study and the
PEACH app. From the sidebar, participants can also
navigate back to the chat channel with the conversa-
tional agent and to the dashboard (Fig. 2). The dash-
board gives an overview of the self-selected personality
change goal and the weekly individual implementation
intention. It also provides a traffic light, whether an indi-
vidual was able to get closer to its intended personality
change goal (green light), further away from it (red light)
or whether there is no change in any direction (yellow
light). For this comparison, participants self-reported
ratings of the Big Five personality states (bipolar adjec-
tive scales) for the last seven days will be compared to
his/her ratings of the Big Five personality states of the
first week of the intervention. The traffic light changes
to green or red when the averaged delta of scores be-
tween the last and the first week is more than half a
standard deviation. The rationale of using a time win-
dow of seven days was to account for natural variance in
daily ratings of study participants. The dashboard also
visualizes whether and on which day participants had
Stieger et al. BMC Psychology (2018) 6:43 Page 12 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
opportunities to pursue and in fact realize their individ-
ual implementation intention during the last seven days.
Eventually, the personal dashboard illustrates their latest
credit score and the remaining time of the intervention
program.
Data Analyses
Longitudinal multilevel modeling (MLM) and structural
equation modeling (SEM) will be used to analyze the
(intensive) longitudinal, nested data structure and
change over time [8184]. Both data-analytic methods
are specifically suitable to model change explicitly as a
function of time and can be used to formulate equivalent
models, providing identical estimates for the collected
data. Separate models will be analyzed including the out-
come assessments at pre-, post- and follow-up (self-re-
ports and observer-reports), daily diary assessments, and
weekly self-report assessments. Predictor/control vari-
ables will be added to the models to examine how indi-
vidual growth will be moderated by variables such as
intervention condition, change goal or willingness to
change. The statistical modeling programs Mplus [85],
and updated R packages (R Core Team, Vienna Austria)
will be used to estimate the growth curve models.
Discussion
This study is the first one testing the effectiveness of a smart-
phone- and conversational agent-based theory-driven inter-
vention for intended personality change to support people
who want to change self-selected personality traits. Under-
standing short-term changeability of personality traits in daily
life and examining whether potential intentional personality
trait changes can be maintained or rather revert over time is
a key goal in the research fields of personality development
and personality dynamics and complements previous long-
standing work on long-term changes of personality traits
across the lifespan. This is particularly important because
personality changes can have a powerful impact on peoples
lives. For example, becoming more conscientious over longer
time periods is related to more health-related behaviors and
ultimately to better health and well-being [86,87]. Intended
personality changes such as decreases in neuroticism may
also reduce economic costs [88].
The study will not only advance our understanding of
the short-term changeability of personality traits and
intended efforts to change them, but also increase our
knowledge of underlying short-term processes and dy-
namics of change. Furthermore, the study contributes to
the understanding of the design of text-based conversa-
tional agents and the role of conversational agents in sup-
porting and coaching individuals to reach their individual
change goals. This is particularly interesting since empir-
ical evidence of text-based conversational agents on the
effectiveness of smartphone-based interventions is still
sparse. The application of the talk-and-tools paradigm
used in the PEACH mobile app can not only complement
and extend existing face-to-face counseling sessions to the
everyday life of individuals, but can also provide new
means to offer smartphone-based coaching interventions
in a scalable fashion where a personal coaching approach
is not feasible due to limited reach, personnel or budget.
Given that the intervention approach of the PEACH
mobile app proves effective, it could be easily imple-
mented in various non-clinical settings such as counsel-
ing/mentoring (e.g., individual change processes) or
coaching (e.g., personality related aspects in diet, fitness,
health and well-being) and could reach large numbers of
people due to its low-threshold character.
Funding
Swiss National Science Foundation (SNSF), Award Number: 162724, Recipients:
Mathias Allemand (PI), Mike Martin, Christoph Flückiger, Elgar Fleisch
Availability of data and materials
Not applicable to this article as the study is ongoing and data are currently
being collected. The Swiss National Science Foundation (SNSF) expects that
data generated by this project are publicly accessible in digital databases
provided there are no legal, ethical, copyright or other issues. An overview of
the output data is available at: http://p3.snf.ch/Project-162724.
Authorscontributions
All authors conceived and designed the study and were involved in
conceptualizing and writing up the present manuscript. MS, MN, TK and DR
drafted the manuscript. CF and MA provided input on drafts of the manuscript
and made revisions. All authors read and approved the final manuscript.
Ethics approval and consent to participate
This study protocol was approved by the Ethics Committee of the
Philosophical Faculty of the University of Zurich, Switzerland (Number of
Approval: 17.8.4, Date of Approval: 31
st
August, 2017). All participants receive
written information about the research project, benefits and risks of
participation. They are informed that they can withdraw from the study at
any time. Informed consent is obtained prior to assessment and intervention.
Consent for publication
Not applicable.
Competing interests
The authors declare that there are no competing financial interests.
PublishersNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Department of Psychology and URPP Dynamics of Healthy Aging, University
of Zurich, Andreasstrasse 15, 8050 Zürich, Switzerland.
2
Technology
Marketing, ETH Zurich, Weinbergstrasse 56/58, 8092 Zürich, Switzerland.
3
Center for Digital Health Interventions, Information Management, ETH
Zurich, Weinbergstrasse 56/58, 8092 Zürich, Switzerland.
4
Center for Digital
Health Interventions, Institute of Technology Management, University of
St.Gallen (ITEM-HSG), Dufourstrasse 40a, St.Gallen, Switzerland.
5
Department
of Psychology, University of Zurich, Binzmühlestrasse 14/04, 8050 Zürich,
Switzerland.
Stieger et al. BMC Psychology (2018) 6:43 Page 13 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Received: 3 July 2018 Accepted: 28 August 2018
References
1. Allan JA, Leeson P, Martin LS. Who wants to change their personality and
what do they want to change? Int Coach Psychol Rev. 2014;9(1):821.
2. Hudson NW, Roberts BW. Goals to change personality traits: concurrent links
between personality traits, daily behavior, and goals to change oneself. J
Res Pers. 2014; https://doi.org/10.1016/j.jrp.2014.08.008.
3. Hudson NW, Fraley RC. Do peoples desires to change their personality traits
vary with age? An examination of trait change goals across adulthood. Soc
Psychol Personal Sci. 2016; https://doi.org/10.1177/1948550616657598.
4. Martin LS, Oades LG, Caputi P. A step-wise process of intentional personality
change coaching. Int Coach Psychol Rev.2014;9(2):18195.
5. Hudson NW, Fraley RC. Volitional personality trait change: can people
choose to change their personality traits? J Pers Soc Psychol. 2015; https://
doi.org/10.1037/pspp0000021.
6. Hudson NW, Fraley RC. Changing for the better? Longitudinal associations
between volitional personality change and psychological well-being. Pers
Soc Psychol Bull. 2016; https://doi.org/10.1177/0146167216637840.
7. Allemand M, Flückiger C. Changing personality traits: some considerations
from psychotherapy process-outcome research for intervention efforts on
intentional personality change. J Psychother Integr. 2017; https://doi.org/10.
1037/int0000094.
8. Grawe K. Psychological psychotherapy. Cambridge, MA: Hogrefe & Huber;
2004.
9. Grawe K, Donati R, Bernauer F. Psychotherapie im Wandel: Von der
Konfession zur Profession. Göttingen: Hogrefe; 1994.
10. Lambert MJ. Bergin and Garfields handbook of psychotherapy and behavior
change. Hoboken, NJ: Wiley; 2013.
11. Norcross JC, Lambert MJ. Psychotherapy relationships that work. 3rd ed.
New York: Oxford University Press; 2018.
12. Flückiger C, Grosse Holtforth MG, Znoj HJ, Caspar F, Wampold BE. Is the
relation between early post-session reports and treatment outcome an
epiphenomenon of intake distress and early response? A multi-predictor
analysis in outpatient psychotherapy. Psychother Res. 2013; https://doi.org/
10.1080/10503307.2012.693773.
13. Free C, Phillips G, Galli L, Watson L, Felix L, Edwards P, et al. The
effectiveness of mobile-health technology-based health behaviour change
or disease management interventions for health care consumers: a
systematic review. PLoS Med. 2013; https://doi.org/10.1371/journal.pmed.
1001362.
14. Martin LS, Oades LG, Caputi P. Intentional personality change coaching: a
randomised controlled trial of participant selected personality facet change
using the five-factor model of personality. Int Coach Psychol Rev.2014;9(2):
196209.
15. Miller WR, Rollnick S. Meeting in the middle: motivational interviewing and
self-determination theory. Int J Behav Nutr Phys Act. 2012; https://doi.org/
10.1186/1479-5868-9-25.
16. De Shazer S. Clues: investigating solutions in brief therapy. New York: WW
Norton & Company, Inc; 1988.
17. Ellis S, Carette B, Anseel F, Lievens F. Systematic reflection: implications for
learning from failures and successes. Curr Dir Psychol Sci. 2014; https://doi.
org/10.1177/0963721413504106.
18. Dweck CS. Can personality be changed? The role of beliefs in personality
and change. Curr Dir Psychol Sci. 2008; https://doi.org/10.1111/j.1467-8721.
2008.00612.x.
19. Donker T, Griffiths KM, Cuijpers P, Christensen H. Psychoeducation for
depression, anxiety and psychological distress: a meta-analysis. BMC Med.
2009; https://doi.org/10.1186/1741-7015-7-79.
20. Bandura A. Social foundations of thought and action: a social cognitive
theory. Englewood Cliffs: Prentice-Hall; 1986.
21. Caspi A, Roberts BW. Personality development across the life course: the
argument for change and continuity. Psychol Inq. 2001; https://doi.org/10.
1207/S15327965PLI1202_01.
22. Bem DJ. Self-perception theory. In: Berkowitz L, editor. Advances in
experimental social psychology. New York: Academic Press; 1972. p. 162.
23. Roberts BW, Wood D, Caspi A. The development of personality traits in
adulthood. In: John OP, Robins RW, Pervin LA, editors. Handbook of
personality: theory and research. New York: Guilford Press; 2008. p. 37598.
24. Gollwitzer PM, Brandstätter V. Implementation intentions and effective goal
pursuit. J Pers Soc Psychol. 1997; https://doi.org/10.1037/0022-3514.73.1.186.
25. Dimidjian S, MJr B. Martell C. Muñoz RF: Lewinsohn PM. The origins and
current status of behavioral activation treatments for depression. Annu Rev
Clin Psychol; 2011. https://doi.org/10.1146/annurev-clinpsy-032210-104535.
26. Magidson JF, Roberts BW, Collado-Rodriguez A, Lejuez CW. Theory-driven
intervention for changing personality: expectancy value theory, behavioral
activation, and conscientiousness. Dev Psychol. 2014; https://doi.org/10.
1037/a0030583.
27. Gassmann D, Grawe K. Ressourcenorientierte Psychotherapie - Schwerpunkt
Soziale Ressourcen. In: Röhrle B, Laireiter T, editors. Soziale Unterstützung
und Psychotherapie:Fortschritte der Gemeindepsychologie und
Gesundheitsförderung. Tübingen: DGVT Verlag; 2009. p. 99122.
28. Risch AK, Wilz G. Ressourcentagebuch: Verbesserung der
Emotionsregulation und der Ressourcenrealisierung durch therapeutisches
Schreiben im Anschluss an eine Psychotherapie. Z Klin Psychol Psychother
(Gott). 2013; https://doi.org/10.1026/1616-3443/a000181.
29. Flückiger C, Wüsten G, Zinbarg R, Wampold B. Resource activation: using
clientsown strengths in psychotherapy and counseling. Göttingen: Hogrefe
Publishing; 2010.
30. McAdams DP. What do we know when we know a person? J Pers. 1995;
https://doi.org/10.1111/1467-6494.ep9510042296.
31. Enock PM, McNally RJ. How mobile apps and other web-based
interventions can transform psychological treatment and the treatment
development cycle. Behav Ther (N Y N Y). 2013;36(3):5666.
32. Klasnja P, Pratt W. Healthcare in the pocket: mapping the space of mobile-
phone health interventions. J Biomed Inform. 2013; https://doi.org/10.1016/j.
jbi.2011.08.017.
33. Künzler F, Kramer J, Kowatsch T. Efficacy of mobile context-aware
notification management systems: a systematic literature review and meta-
analysis. Paper presented at the IEEE 13th International Conference on
Wireless and Mobile Computing, Networking and Communications
(WiMob). Rome; 2017; doi:https://doi.org/10.1109/wimob.2017.8115839
34. Marsch LA, Lord SE, Dallery J. Behavioral health care and technology: using
science-based innovations to transfer practice. Oxford: Oxford University
Press; 2015.
35. Miller G. The smartphone psychology manifesto. Perspect Psychol Sci. 2012;
https://doi.org/10.1177/1745691612441215.
36. Schembre SM, Liao Y, Robertson MC, Dunton GF, Kerr J, Haffey ME, et al.
Just-in-time feedback in diet and physical activity interventions: systematic
review and practical design framework. JMIR. 2018; https://doi.org/10.2196/
jmir.8701.
37. Schueller SM, Muñoz RF, Mohr DC. Realizing the potential of behavioral
intervention technologies. Curr Dir Psychol Sci. 2013; https://doi.org/10.
1177/0963721413495872.
38. Wahle F, Kowatsch T, Fleisch E, Rufer M, Weidt S. Mobile sensing and
support for people with depression: a pilot trial in the wild. JMIR Mhealth
Uhealth. 2016; https://doi.org/10.2196/mhealth.5960.
39. Harvey EJ, Rubin LF, Smiley SL, Zhou Y, Elmasry H, Pearson JL. Mobile phone
ownership is not a serious barrier to participation in studies: descriptive
study. JMIR Mhealth Uhealth. 2018; https://doi.org/10.2196/mhealth.8123.
40. Kowatsch T, Nißen MK, Shih I, Rüegger D, Volland D, Filler A, et al. Text-
based healthcare chatbots supporting patient and health professional
teams: preliminary results of a randomized controlled trial on childhood
obesity, persuasive embodied agents for behavior change (PEACH2017)
workshop. Co-located with the 17th International Conference on Intelligent
Virtual Agents (IVA 2017). Stockholm; 2017: doi:https://doi.org/10.3929/ethz-
b-000218776.
41. Shih I, Volland D, Rüegger D, Künzler F, Barata F, Filler A, et al. Therapy
adherence of obese children in a 6-month high-frequency intervention.
Lucerne: Poster presented at the CSS health insurance meets CDHI Event;
2017. https://doi.org/10.3929/ethz-b-000221259.
42. Beun RJ, Fitrianie S, Griffioen-Both F, Spruit S, Horsch C, Lancee J, et al. Talk
and tools: the best of both worlds in mobile user interfaces for e-coaching.
Pers Ubiquitous Comput. 2017; https://doi.org/10.1007/s00779-017-1021-5.
43. Kowatsch T, Volland D, Shih I, Rüegger D, Künzler F, Barata F, et al. Design
and evaluation of a mobile chat app for the open source behavioral health
intervention platform MobileCoach, In: Maedche A, Vom Brocke J, Hevner A,
editors. Designing the digital transformation. DESRIST 2017. Lecture Notes in
Computer Science. Berlin; Germany: Springer; 2017. https://doi.org/10.1007/
978-3-319-59144-5_36.
Stieger et al. BMC Psychology (2018) 6:43 Page 14 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
44. Bickmore TW, Schulman D, Sidner CL. A reusable framework for health
counseling dialogue systems based on a behavioral medicine ontology. J
Biomed Inform. 2011; https://doi.org/10.1016/j.jbi.2010.12.006.
45. Cassell J, Sullivan J, Churchill E. Embodied conversational agents. Boston:
MIT Press; 2000.
46. Ebling MR. Can cognitive assistants disappear? IEEE Pervasive Comput. 2016;
https://doi.org/10.1109/MPRV.2016.41.
47. Miner AS, Milstein A, Schueller S, Hegde R, Mangurian C, Linos E.
Smartphone-based conversational agents and responses to questions about
mental health, interpersonal violence, and physical health. JAMA Intern
Med. 2016; https://doi.org/10.1001/jamainternmed.2016.0400.
48. Provoost S, Lau HM, Ruwaard J, Riper H. Embodied conversational agents in
clinical psychology: a scoping review. J Med Internet Res. 2017; https://doi.
org/10.2196/jmir.6553.
49. Kowatsch T, Nißen MK, Rüegger D, Stieger M, Flückiger C, Allemand M, von
Wangenheim F. The impact of interpersonal closeness cues in text-based
healthcare chatbots on attachment bond and the desire to continue
interacting: an experimental design. Paper presented at the 26th European
Conference on Information Systems (ECIS), Portsmouth, UK; 2018.
50. Reeves B, Nass C. The media equation: how people treat computers,
television, and new media like real people and places. Cambridge: CSLI
Publications and Cambridge University Press; 1996.
51. Flückiger C, Del Re AC, Horvath AO, Wampold BE. The alliance in adult
psychotherapy: a meta-analytic synthesis. Psychotherapy. 2018; https://doi.
org/10.1037/pst0000172.
52. Horvath AO, Greenberg LS. The working alliance: theory, research, and
practice (Vol. 173). New York: John Wiley & Sons; 1994.
53. Martin DJ, Garske JP, Davis MK. Relation of the therapeutic alliance with
outcome and other variables: a meta-analytic review. J Couns Psychol. 2000;
https://doi.org/10.1037/0022-006X.68.3.438.
54. Bickmore TW, Picard RW. Establishing and maintaining long-term human-
computer relationships. ACM Trans Comput Hum Interact. 2005; https://doi.
org/10.1145/1067860.1067867.
55. Bickmore T, Schulman D, Yin L. Maintaining engagement in long-term
interventions with relational agents. Appl Artif Intell. 2010; https://doi.org/
10.1080/08839514.2010.492259.
56. Turner JC, Hogg MA, Oakes PJ, Reicher SD, Wetherell MS. Rediscovering the
social group: a self-categorization theory. Cambridge: Basil Blackwell; 1987.
57. Sah YJ, Peng W. Effects of visual and linguistic anthropomorphic cues on social
perception, self-awareness, and information disclosure in a health website.
Comput Human Behav. 2015; https://doi.org/10.1016/j.chb.2014.12.055.
58. Haug S, Paz Castro R, Kowatsch T, Filler A, Dickson-Spillmann M, Dey M,
Schaub MP. Efficacy of a web- and text messaging-based intervention to
reduce problem drinking in adolescents: results of a cluster-randomised
controlled trial. J Consult Clin Psychol. 2017; https://doi.org/10.1037/
ccp0000138.
59. Soto CJ, John OP. The next Big Five Inventory (BFI-2): developing and
assessing a hierarchical model with 15 facets to enhance bandwidth,
fidelity, and predictive power. J Pers Soc Psychol. 2017; https://doi.org/10.
1037/pspp0000096.
60. Brunstein JC. Persönliche Ziele und Handlungs- versus Lageorientierung. J
Individ Differ. 2001; https://doi.org/10.1024//0170-1789.22.1.1.
61. Soto CJ, John OP. Short and extra-short forms of the Big Five Inventory2: the
BFI-2-S and BFI-2-XS. J Res Pers. 2017; https://doi.org/10.1016/j.jrp.2017.02.004.
62. Lutz W, Tholen S, Schürch E, Berking M. Reliabilität von Kurzformen
gängiger psychometrischer Instrumente zur Evaluation des therapeutischen
Fortschritts in Psychotherapie und Psychiatrie. Diagnostica. 2006; https://doi.
org/10.1026/0012-1924.52.1.11.
63. Hautzinger M, Bailer M. Allgemeine Depressionsskala. Beltz: Weinheim; 1993.
64. Dweck CS. Self-theories: their role in motivation, personality and
development. Philadelphia: Taylor & Francis/Psychology Press; 1999.
65. Diener ED, Emmons RA, Larsen RJ, Griffin S. The satisfaction with life scale. J
Psychol Assess. 1985; https://doi.org/10.1207/s15327752jpa4901_13.
66. Rosenberg M. Society and adolescent self-image. Princeton University Press:
Princeton; 1965.
67. Hatcher RL, Gillaspy JA. Development and validation of a revised short
version of the working alliance inventory. Psychother Res. 2006; https://doi.
org/10.1080/10503300500352500.
68. Kiluk BD, Serafini K, Frankforter T, Nich C, Carroll KM. Only connect: the
working alliance in computer-based cognitive behavioral therapy. Behav Res
Ther. 2014; https://doi.org/10.1016/j.brat.2014.10.003.
69. Bartneck C, KulićD, Croft E, Zoghbi S. Measurement instruments for the
anthropomorphism, animacy, likeability, perceived intelligence, and
perceived safety of robots. Int J Soc Robot. 2009; https://doi.org/10.1007/
s12369-008-0001-3.
70. McKnight DH, Choudhury V, Kacmar C. Developing and validating trust
measures for e-commerce: an integrative typology. Inf Syst Res. 2002;
https://doi.org/10.1287/isre.13.3.334.81.
71. Aron A, Aron EN, Smollan D. Inclusion of other in the self scale and the
structure of interpersonal closeness. J Pers Soc Psychol. 1992; https://doi.
org/10.1037/0022-3514.63.4.596.
72. Gächter S, Starmer C, Tufano F. Measuring the closeness of relationships: a
comprehensive evaluation of the inclusion of the other in the self scale.
PLoS One. 2015; https://doi.org/10.1371/journal.pone.0129478.
73. Govern JM, Marsch LA. Development and validation of the situational self-
awareness scale. Conscious Cogn. 2001; https://doi.org/10.1006/ccog.2001.0506.
74. Wixom BH, Todd PA. A theoretical integration of user satisfaction and technology
acceptance. Inf Syst Res. 2005; https://doi.org/10.1287/isre.1050.0042.
75. Pollak JP, Adams P, Gay G. PAM: a photographic affect meter for frequent,
in situ measurement of affect. In: In Proceedings of the SIGCHI conference
on human factors in computing systems. Vancouver BC: ACM; 2011. https://
doi.org/10.1145/1978942.1979047.
76. Chittaranjan G, Blom J, Gatica-Perez D. Whos who with big-five: analyzing
and classifying personality traits with smartphones. In Wearable Computers
(ISWC), 15th Annual International Symposium on IEEE. 2011; doi:https://doi.
org/10.1109/ISWC.2011.29.
77. Rüegger D, Stieger M, Flückiger C, Allemand M, Kowatsch, T. Leveraging the
potential of personality traits for digital health interventions: a literature
review on digital markers for conscientiousness and neuroticism. In 11th
Mediterranean Conference on Information Systems (MCIS). Genoa; 2017; doi:
10.3929/ethz-b-000218434.
78. Filler A, Kowatsch T, Haug S, Wahle F, Staake T, Fleisch E. MobileCoach: a
novel open source platform for the design of evidence-based, scalable and
low-cost behavioral health interventions - overview and preliminary
evaluation in the public health context. Paper presented at the 14th annual
Wireless Telecommunications Symposium (WTS 2015). New York; 2015; doi:
https://doi.org/10.1109/WTS.2015.7117255.
79. Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models
to guide the development of just-in-time adaptive interventions: a
pragmatic framework. Health Psychol. 2015; https://doi.org/10.1037/
hea0000306.
80. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al.
Just-in-time adaptive interventions (JITAIs) in mobile health: key
components and design principles for ongoing health behavior support.
Ann Behav Med. 2016; https://doi.org/10.1007/s12160-016-9830-8.
81. Bolger N, Laurenceau J-P. Intensive longitudinal methods: an introduction
to diary and experience sampling research. New York: Guilford Press; 2013.
82. Little TD. Longitudinal structural equation modeling. New York: Guilford
Press; 2013.
83. Nezlek JB. Multilevel modeling for social and personality psychology.
London: Sage Publications, Ltd; 2011.
84. Raudenbush SW, Bryk AS. Hierarchical linear models: applications and data
analysis methods (Vol. 1). California: Sage Publications, Inc; 2002.
85. Muthén LK, Muthén BO. Mplus: statistical analysis with latent variables: user's
guide. Los Angeles: Muthén & Muthén; 2010. p. 19982007.
86. Ozer DJ, Benet-Martinez V. Personality and the prediction of consequential
outcomes. Annu Rev Psychol. 2006; https://doi.org/10.1146/annurev.psych.
57.102904.190127.
87. Roberts BW, Kuncel NR, Shiner R, Caspi A, Goldberg LR. The power of
personality: the comparative validity of personality traits, socioeconomic
status, and cognitive ability for predicting important life outcomes. Perspect
Psychol Sci. 2007; https://doi.org/10.1111/j.1745-6916.2007.00047.
88. Cuijpers P, Smit F, Penninx BW, de Graaf R, ten Have M, Beekman AT.
Economic costs of neuroticism: a population-based study. Arch Gen
Psychiatry. 2010; https://doi.org/10.1001/archgenpsychiatry.2010.130.
89. Malhotra NK, Kim SS, Agarwal J. Internet users' information privacy concerns
(IUIPC): the construct, the scale, and a causal model. Inf Syst Res. 2004;
https://doi.org/10.1287/isre.1040.0032.
90. Collier JE, Sherrell DL. Examining the influence of control and convenience
in a self-service setting. J Acad Mark Sci. 2010; https://doi.org/10.1007/
s11747-009-0179-4.
Stieger et al. BMC Psychology (2018) 6:43 Page 15 of 15
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
... The primary aim of this study is to investigate the association between short-term momentary goals and personality. Despite growing theoretical interest in the bidirectional link between goals and personality (Allemand & Flückiger, 2017;Stieger et al., 2018), the role of specific goals in personality enactment remains under-researched. The goal-personality link is usually investigated as a function of super-ordinate properties of goals -such as their approach or avoidance nature (Heller et al., 2009) -or in relation to more general motivations toward trait change (Hudson et al., 2020) or of broad life goals (Bleidorn, 2009). ...
... Indeed, the possibility for personality traits to change (including conscientiousness) is now demonstrated Stieger et al., 2020). Our findings may be of interest to those who develop protocols targeting change (Stieger et al., 2018(Stieger et al., ,2020. Specifically, they suggest that targeting momentary goals may be a feasible way to promote trait change through bottom-up processes (e.g., increasing their salience, promoting goal selection and pursuit, etc.). ...
Article
Objective: Personality involves both trait and state components, personal goals serving a crucial regulatory function for the expression of personality states. The present study investigates the dynamic interplay between conscientiousness-related goals, conscientious personality states, and trait conscientiousness. Method: A sample of 244 community participants responded to a baseline survey (T1), a 5-times-a-day Ecological Momentary Assessment (EMA) for 15 days, and a post-EMA survey (T2). Results: Pre-registered multilevel analyses indicated significant contemporaneous positive and negative associations between momentary conscientious and unconscientious goals and state conscientiousness, respectively. Cross-lagged associations also emerged, with goals predicting future states of conscientiousness. A latent growth model was fitted on a subsample of participants (N = 159). Results indicated that change in trait conscientiousness from T1 to T2 was explained by growth in conscientiousness-related goals during the EMA phase, with a mediating effect of growth in state conscientiousness. Conclusions: Overall, the results corroborate the importance of goals for modeling contemporaneous and cross-lagged personality dynamics, both in short and longer timeframes.
... In particular, conversational agents (CAs) have been applied to a variety of chronic disease contexts to help coach individuals and offer behavioral lifestyle interventions (10)(11)(12). Such applications have been shown to build working alliances with users (13), leverage benefits of gamification (14), utilize techniques from psychotherapy (e.g., cognitive behavioral therapy, motivational interviewing) (15) and enhance behavioral coaching in a manner similar to human-delivered coaching (11,12,(16)(17)(18)(19). Importantly, these interventions can be designed in a low-cost and accessible manner (20), so they have high potential to scale widely and offer a healthcare service to those whom may be lacking in treatment coverage (21,22). ...
... Without guidance therefore, there exists the ever present danger that such stressors may lead to unhealthy coping strategies, creating a negative feedback loop and increased strains upon mental health (112). While mental health has previously been considered a delicate topic for automated agents to address, there exists a growing literature body on utilizing CAs to deliver high quality care (15,19,114). Contributing to this emergent stream of research, the mental health module offers the topics of: (i) anxiety, (ii) loneliness and (iii) discovering mental resources. ...
Article
Full-text available
Background: The current COVID-19 coronavirus pandemic is an emergency on a global scale, with huge swathes of the population required to remain indoors for prolonged periods to tackle the virus. In this new context, individuals' health-promoting routines are under greater strain, contributing to poorer mental and physical health. Additionally, individuals are required to keep up to date with latest health guidelines about the virus, which may be confusing in an age of social-media disinformation and shifting guidelines. To tackle these factors, we developed Elena+, a smartphone-based and conversational agent (CA) delivered pandemic lifestyle care intervention. Methods: Elena+ utilizes varied intervention components to deliver a psychoeducation-focused coaching program on the topics of: COVID-19 information, physical activity, mental health (anxiety, loneliness, mental resources), sleep and diet and nutrition. Over 43 subtopics, a CA guides individuals through content and tracks progress over time, such as changes in health outcome assessments per topic, alongside user-set behavioral intentions and user-reported actual behaviors. Ratings of the usage experience, social demographics and the user profile are also captured. Elena+ is available for public download on iOS and Android devices in English, European Spanish and Latin American Spanish with future languages and launch countries planned, and no limits on planned recruitment. Panel data methods will be used to track user progress over time in subsequent analyses. The Elena+ intervention is open-source under the Apache 2 license (MobileCoach software) and the Creative Commons 4.0 license CC BY-NC-SA (intervention logic and content), allowing future collaborations; such as cultural adaptions, integration of new sensor-related features or the development of new topics. Discussion: Digital health applications offer a low-cost and scalable route to meet challenges to public health. As Elena+ was developed by an international and interdisciplinary team in a short time frame to meet the COVID-19 pandemic, empirical data are required to discern how effective such solutions can be in meeting real world, emergent health crises. Additionally, clustering Elena+ users based on characteristics and usage behaviors could help public health practitioners understand how population-level digital health interventions can reach at-risk and sub-populations.
... The experimental stimuli were designed based on a prototype of a fictitious health care chatbot promoting a personality change intervention adapted from Stieger et al [102]. For purposes of standardization, the interaction with the chatbot was purely text based (ie, no voice input or output) and followed a rule-based conversational script with predefined answer options. ...
Article
Full-text available
Background: The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. Objective: This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients' experiences and the development of an affective bond with the chatbot, depending on clients' characteristics (ie, age and gender) and whether they can freely choose a chatbot's social role. Methods: Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings-institution, expert, peer, and dialogical self-and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. Results: While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants' demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117). Conclusions: Manipulating a chatbot's social role is a possible avenue for health care chatbot designers to tailor clients' chatbot experiences using user-specific demographic factors and to improve clients' perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots.
... So können Chatbots im Alltag der Nutzenden mit ihnen kommunizieren, ohne dass ein Therapeut direkt involviert sein muss (47,48). Auf diese Weise können kürzere psychotherapeutische Interventionen angeboten werden, die keine komplexeren therapeutischen Fähigkeiten erfordern (49,50). Beispiele dafür können Psychoedukationsgespräche, Zielsetzungsgespräche, Anleitungen zur Verhaltensaktivierung oder Entspannung sein, die nicht zwangsweise Therapeutenkontakt benötigen. ...
Article
Full-text available
Der Wechsel vom stationären in das ambulante Setting stellt für Betroffene mit psychischen Störun- gen eine kritische Übergangszeit dar, in der es häufig zu Rückfällen, suizidalen Krisen oder Rehospitalisierungen kommt. Vielen Patienten steht nach einem stationären Aufenthalt nicht unmittelbar ein ambulanter Therapieplatz zur Verfügung, und Nachsorgeangebote sind selten Teil der Regelversorgung. Die Digitalisierung bietet diesbezüglich neue Möglichkeiten, das bestehende Behandlungsangebot zu erweitern. Besonders der technologische Fortschritt im Bereich von Smartphones und Apps ermöglicht neue Formen der Informationsvermittlung, die bestehende Versorgungslücken schliessen könnten. Der nachfolgende Artikel soll einen kurzen Überblick über die Rehospitalisierungsproblematik geben und aufzeigen, wie App-basierte Interventionen bisher in der Nachsorge eingesetzt wurden. Zudem wird dargelegt, inwiefern neue technische Entwicklungen, wie die Chatbot-Technologie und die Erhebung passiver Daten, den Effekt App-basierter Interventionen in Zukunft verbessern können.
... In fact, researchers agree that mental health care CAs should be used primarily as support systems, since the interaction experience and relationship that develops between a therapist and a patient is considered a significant factor in the outcome of psychological therapy (D'Alfonso, 2020) and cannot easily be substituted by a machine. The role of CAs in mental health care, rather, is to address individuals in need of treatment who are not receiving any treatment at all due to various barriers (Bendig et al., 2019;Stieger et al., 2018). In this way CAs could provide low-threshold access to mental health care but also bridge the waiting time before approval of psychotherapy (Bendig et al., 2019;Grünzig et al., 2018). ...
Article
Full-text available
Millions of people experience mental health issues each year, increasing the necessity for health-related services. One emerging technology with the potential to help address the resulting shortage in health care providers and other barriers to treatment access are conversational agents (CAs). CAs are software-based systems designed to interact with humans through natural language. However, CAs do not live up to their full potential yet because they are unable to capture dynamic human behavior to an adequate extent to provide responses tailored to users’ personalities. To address this problem, we conducted a design science research (DSR) project to design personality-adaptive conversational agents (PACAs). Following an iterative and multi-step approach, we derive and formulate six design principles for PACAs for the domain of mental health care. The results of our evaluation with psychologists and psychiatrists suggest that PACAs can be a promising source of mental health support. With our design principles, we contribute to the body of design knowledge for CAs and provide guidance for practitioners who intend to design PACAs. Instantiating the principles may improve interaction with users who seek support for mental health issues.
... The participants used a variety of devices from different manufacturers: Samsung (31 participants), Huawei (21), Xiaomi (7), OnePlus (7) Compensation. We implemented an incremental reward system and the chance to win an additional price via a lottery similar to other works [32,64,69]. Participants were rewarded for their participation depending on their level of contribution and received between CHF 60 and CHF 120 for submitting an average of 3 and 6 self-reports per day, respectively. ...
Conference Paper
Full-text available
Knowledge of users’ affective states can improve their interaction with smartphones by providing more personalized experiences (e.g., search results and news articles). We present an affective state classification model based on data gathered on smartphones in real-world environments. From touch events during keystrokes and the signals from the inertial sensors, we extracted two-dimensional heat maps as input into a convolutional neural network to predict the affective states of smartphone users. For evaluation, we conducted a data collection in the wild with 82 participants over 10 weeks. Our model accurately predicts three levels (low, medium, high) of valence (AUC up to 0.83), arousal (AUC up to 0.85), and dominance (AUC up to 0.84). We also show that using the inertial sensor data alone, our model achieves a similar performance (AUC up to 0.83), making our approach less privacy-invasive. By personalizing our model to the user, we show that performance increases by an additional 0.07 AUC.
... 24,25,[27][28][29]31,[34][35][36][37]39,45,46,64,66,68 Data from sensors or wearables 3 (4.3%) 24,30,53 Video data 1 (1%) 24 Personal health data 52 (74.3%) 6,10,16,26,29,31,32,37,39,[43][44][45][46][47][48][49][52][53][54][55][56][57][58][59][60][61][62][63][64][66][67][68][70][71][72][73][74][75][76][77][78][79][80][81][82][83][84][85][86][87][88][89] Analyzed data from conversations (e.g. symptoms, emotions) 6 (8.6%) 28,29,34,35,40,45 Answers to standardized questionnaires (PHQ) and calculated scores 2 (2.9%) 33,51 Type of app With app type we refer to the focus and methodological specification of the included conversational agents. ...
Article
Health chatbots interview patients and collect health data. This process makes demands on data security and data privacy. To identify how and to what extent security and privacy are considered in current health chatbots. We conducted a scoping review by searching three bibliographic databases (PubMed, ACM Digital Library, IEEExplore) for papers reporting on chatbots in healthcare. We extracted which, how, and where data is stored by health chatbots and identified which external services have access to the data. Out of 1026 retrieved papers, we included 70 studies in the qualitative synthesis. Most papers report on chatbots that collect and process personal health data, usually in the context of mental health coaching applications. The majority did not provide any information regarding security or privacy aspects. We were able to determine limitations in literature and identified concrete challenges, including data access and usage of (third-party) services, data storage, data security methods, use case peculiarities and data privacy, as well as legal requirements. Data privacy and security in health chatbots are still underresearched and related information is underrepresented in scientific literature. By addressing the five key challenges in future, the transfer of theoretical solutions into practice can be facilitated.
... Findings of longitudinal associations between environmental concerns and personality traits raise questions about causality. If changes in personality cause changes in concern, and ultimately behavior, personality interventions could help promote sustainable behavior and other prosocial outcomes (Hudson & Fraley, 2015;Stieger et al., 2018), particularly when connected to tractable, practical models of sustainability (Clark et al., 2016). It is also possible that changes in concern about the environment cause changes in personality. ...
Article
Full-text available
Using data from 58,748 participants from a nationally representative German sample, we tested preregistered hypotheses about factors that impact concerns about the environment over time. We found that environmental concerns increased modestly from 2009 to 2017. Individuals in middle adulthood tended to be more concerned and showed more consistent increases in concern over time than younger or older people. Consistent with previous research, personality traits were correlated with environmental concerns. We present novel evidence that increases in concern were related to increases in the personality traits neuroticism, openness to experience, and potentially agreeableness. These findings highlight the importance of understanding individual level factors associated with changes in environmental concerns over time, towards the promotion of more sustainable behavior.
Article
Research on the relationship between personality traits and cognitive abilities has primarily used cross-sectional designs and considered personality traits individually in relation to cognitive dimensions. This study (N = 2652) examined the relationship between Big Five personality change profiles and change in cognitive factors, episodic memory and executive functioning. Latent profile analysis was used to capture patterns of change across the Big Five traits. Three profiles of personality change were defined: Decreasers, Maintainers, and Increasers. The Decreasers declined more in episodic memory compared to the Increasers and Maintainers. Also, the Decreasers declined more in executive functioning compared to the Increasers, but not the Maintainers. The findings advance our understanding of the links between patterns of personality change and cognitive aging.
Article
Withstanding the climate crisis will depend in part on individuals behaving in a more environmentally sustainable manner. However, relatively little is known about the individual factors that promote sustainable attitudes and behaviors (SABs). Although there are established cross-sectional associations between personality traits and SABs, it is unclear whether changes in personality are related to increases in SABs over time, and how personality is differentially related to specific SABs. Using data from 61,479 participants in New Zealand, we tested preregistered hypotheses about how personality codevelops with valuing the environment, believing in climate change, concern about climate change, personal environmental efficacy, personal environmental sacrifice, and support for the Green Party. We found that SABs generally increased from 2009 to 2017, although there was variation across age cohorts, SAB variables, and samples. We replicated concurrent correlations between broad personality traits—particularly Agreeableness, Openness, and Honesty/Humility—and SABs and present novel evidence that increases in SAB are related to changes in traits, particularly Agreeableness. These findings have implications for both understanding the factors associated with changes in SABs over time and understanding the factors that drive personality change.
Article
Full-text available
Objectives: Recent literature suggests that personality may be more amenable to change than was previously thought, and that participant selected intentional personality change may be beneficial. The aim of this study was to examine the effects of a 10-week structured intentional personality change coaching programme on participant selected personality facets. Design: Participants were assigned to the personality coaching group or a waitlist control group using a waitlist control, matched, randomised procedure (personality coaching group, N=27; waitlist control group, N=27). Method: A structured coaching programme, designed to identify and modify a limited number of personality facets, chosen by the client, was employed. Results: Participation in the personality change coaching programme was associated with significant positive change in participant selected facets, with gains maintained three months later. Neither age of participant nor number of facets targeted significantly affected change outcomes. Conclusions: These findings suggest that a structured personality change coaching programme may facilitate beneficial personality change in motivated individuals. Keywords: Intentional personality change coaching.
Article
Full-text available
Recent findings suggest that personality is amenable to change via interventions and that such change may be beneficial. However, there is a gap in the literature concerning what aspects of their personality individuals in non-clinical populations wish to change, and if the personality of individuals who choose to change their personality differs from the normal population. Clarification of these questions may help inform the development of personality change resources and interventions. The current study explored the personality profiles (as measured by the NEO PI-R) of 54 volunteers for an intentional personality change coaching study, and describes the personality facets they chose to target for change. The results of this study indicated that participants had significantly higher openness and emotionality. Targeted personality facets primarily fell within the domains of emotionality (48.17 per cent) and conscientiousness (28.04 per cent). Anxiety (N=28), self-discipline (N=19), angry/hostility (N=17), depression (N=11) and self-consciousness (N=11) were the most commonly targeted facets. These results inform the literature regarding which individuals may be motivated to change their personalities and for what purpose. There may also be wider implications regarding how the personality of volunteers for intervention research may differ from the general population.
Conference Paper
Full-text available
Working alliance describes an important relationship quality between health professionals and patients and is robustly linked to treatment success. However, due to limited resources of health professionals, working alliance cannot always be promoted just-in-time in a ubiquitous fashion. To address this scalability problem, we investigate the direct effect of interpersonal closeness cues of text-based healthcare chatbots (THCBs) on attachment bond from the working alliance con-struct and the indirect effect on the desire to continue interacting with THCBs. The underlying research model and hypotheses are informed by counselling psychology and research on conver-sational agents. In order to investigate the hypothesized effects, we first develop a THCB codebook with 12 design dimensions on interpersonal closeness cues that are categorized into visual cues (i.e. avatar), verbal cues (i.e. greetings, address, jargon, T-V-distinction), quasi-nonverbal cues (i.e. emoticons) and relational cues (i.e. small talk, self-disclosure, empathy, humor, meta-relational talk and continuity). In a second step, four distinct THCB designs are developed along the continuum of interpersonal closeness (i.e. institutional-like, expert-like, peer-like and myself-like THCBs) and a corresponding study design for an interactive THCB-based online experiment is presented to test our hypotheses. We conclude this work-in-progress by outlining our future work.
Preprint
Full-text available
Open access: http://psycnet.apa.org/fulltext/2018-23951-001.pdf Abstract: The alliance continues to be one of the most investigated variables related to success in psychotherapy irrespective of theoretical orientation. We define and illustrate the alliance (also conceptualized as therapeutic alliance, helping alliance or working alliance) and then present a meta-analysis of 295 independent studies that covered more than 30,000 patients (published between 1978 and 2017) for face-to-face psychotherapy as well as internet-based psychotherapy. The relation of the alliance and treatment outcome was investigated using three-level meta- analysis with random-effects restricted maximum-likelihood estimators. The overall alliance- outcome association for face-to-face psychotherapy was r = .278 (95% CIs [.256, .299], p < .0001; equivalent of d = .579). There was heterogeneity among the ESs, and 2% of the 295 ESs indicated negative correlations. The correlation for internet-based psychotherapy was approximately the same (viz., r = .275, k = 23). These results confirm the robustness of the positive relation between the alliance and outcome. This relation remains consistent across assessor perspectives, alliance and outcome measures, treatment approaches, patient characteristics, and countries. The article concludes with causality considerations, research limitations, diversity considerations, and therapeutic practices. Keywords: therapeutic alliance, psychotherapy relationship, working alliance, meta-analysis, psychotherapy outcome, face-to-face therapy, internet-based therapy
Article
Full-text available
Background: The integration of body-worn sensors with mobile devices presents a tremendous opportunity to improve just-in-time behavioral interventions by enhancing bidirectional communication between investigators and their participants. This approach can be used to deliver supportive feedback at critical moments to optimize the attainment of health behavior goals. Objective: The goals of this systematic review were to summarize data on the content characteristics of feedback messaging used in diet and physical activity (PA) interventions and to develop a practical framework for designing just-in-time feedback for behavioral interventions. Methods: Interventions that included just-in-time feedback on PA, sedentary behavior, or dietary intake were eligible for inclusion. Feedback content and efficacy data were synthesized descriptively. Results: The review included 31 studies (15/31, 48%, targeting PA or sedentary behavior only; 13/31, 42%, targeting diet and PA; and 3/31, 10%, targeting diet only). All studies used just-in-time feedback, 30 (97%, 30/31) used personalized feedback, and 24 (78%, 24/31) used goal-oriented feedback, but only 5 (16%, 5/31) used actionable feedback. Of the 9 studies that tested the efficacy of providing feedback to promote behavior change, 4 reported significant improvements in health behavior. In 3 of these 4 studies, feedback was continuously available, goal-oriented, or actionable. Conclusions: Feedback that was continuously available, personalized, and actionable relative to a known behavioral objective was prominent in intervention studies with significant behavior change outcomes. Future research should determine whether all or some of these characteristics are needed to optimize the effect of feedback in just-in-time interventions.
Article
Full-text available
Background Rather than providing participants with study-specific data collection devices, their personal mobile phones are increasingly being used as a means for collecting geolocation and ecological momentary assessment (EMA) data in public health research. Objective The purpose of this study was to (1) describe the sociodemographic characteristics of respondents to an online survey screener assessing eligibility to participate in a mixed methods study collecting geolocation and EMA data via the participants’ personal mobile phones, and (2) examine how eligibility criteria requiring mobile phone ownership and an unlimited text messaging plan affected participant inclusion. Methods Adult (≥18 years) daily smokers were recruited via public advertisements, free weekly newspapers, printed flyers, and word of mouth. An online survey screener was used as the initial method of determining eligibility for study participation. The survey screened for twenty-eight inclusion criteria grouped into three categories, which included (1) cell phone use, (2) tobacco use, and (3) additional criteria ResultsA total of 1003 individuals completed the online screener. Respondents were predominantly African American (605/1003, 60.3%) (60.4%), male (514/1003, 51.3%), and had a median age of 35 years (IQR 26-50). Nearly 50% (496/1003, 49.5%) were unemployed. Most smoked menthol cigarettes (699/1003, 69.7%), and had a median smoking history of 11 years (IQR 5-21). The majority owned a mobile phone (739/1003, 73.7%), could install apps (86.8%), used their mobile phone daily (89.5%), and had an unlimited text messaging plan (871/1003, 86.8%). Of those who completed the online screener, 302 were eligible to participate in the study; 163 were eligible after rescreening, and 117 were enrolled in the study. Compared to employed individuals, a significantly greater proportion of those who were unemployed were ineligible for the study based on mobile phone inclusion criteria (P
Conference Paper
Full-text available
Health professionals have limited resources and are not able to personally monitor and support patients in their everyday life. Against this background and due to the increasing number of self-service channels and digital health interventions, we investigate how text-based healthcare chatbots (THCB) can be designed to effectively support patients and health professionals in therapeutic settings beyond on-site consultations. We present an open source THCB system and how the THCP was designed for a childhood obesity intervention. Preliminary results with 15 patients indicate promising results with respect to intervention adherence (ca. 13.000 conversational turns over the course of 4 months or ca. 8 per day and patient), scalability of the THCB approach (ca. 99.5% of all conversational turns were THCB-driven) and over-average scores on perceived enjoyment and attachment bond between patient and THCB. Future work is discussed.
Conference Paper
Full-text available
Digital health interventions (DHIs) are designed to help individuals manage their disease, such as asthma, diabetes, or major depression. While there is a broad body of literature on how to design evidence-based DHIs with respect to behavioral theories, behavior change techniques or various design features, targeting personality traits has been neglected so far in DHI designs, although there is evidence of their impact on health. In particular, conscientiousness, which is related to therapy adherence, and neuroticism, which impacts long-term health of chronic patients, are two personality traits with an impact on health. Sensing these traits via digital markers from online and smartphone data sources and providing corresponding personality change interventions, i.e. to increase conscientiousness and to reduce neuroticism, may be an important active and generic ingredient for various DHIs. As a first step towards this novel class of personality change DHIs, we conducted a systematic literature review on relevant digital markers related to conscientiousness and neuroticism. Overall, 344 articles were reviewed and 21 were selected for further analysis. We found various digital markers for conscientiousness and neuroticism and discuss them with respect to future work, i.e. the design and evaluation of personality change DHIs.
Conference Paper
Notifications can be relevant but they can also decrease productivity when delivered at the wrong point in time. Smartphones are increasingly capable of detecting relevant context information with the goal to decrease the number of these badly timed interruptions. Accordingly, research on context- aware notification management systems (CNMSs) on mobile devices has received increasing attention recently, prototypes have been built and empirically evaluated. However, there exists no systematic overview of mobile CNMSs evaluating their efficacy. The objectives of the current work are therefore to identify relevant empirical studies that have assessed the efficacy of mobile CNMSs and to discuss the findings with respect to future work. A systematic literature review and meta-analysis was conducted to address these objectives. Consistent with prior work, two efficacy metrics were applied: response rate and response delay. A keyword-based search strategy was used and resulted in 1’634 studies, out of which 8 were relevant for the topic. Findings indicate that mobile CNMSs increase the response rate, while there was only little evidence that they reduce response time, too. Implications for researchers and practitioners are discussed and future research is outlined that aims at further increasing the efficacy of mobile CNMSs.