The kleineWeltentdecker App - A smartphone-based developmental diary

Abstract

Today, a vast number of tools exist to measure development in early childhood in a variety of domains such as cognition, language, or motor, cognition. These tools vary in different aspects. Either children are examined by a trained experimenter, or caregivers fill out questionnaires. The tools are applied in the controlled setting of a laboratory or in the children’s natural environment. While these tools provide a detailed picture of the current state of children’s development, they are at the same time subject to several constraints. Furthermore, the measurement of an individual child’s change of different skills over time requires not only one measurement but high-density longitudinal assessments. These assessments are time-consuming, and the breadth of developmental domains assessed remains limited. In this paper, we present a novel tool to assess the development of skills in different domains, a smartphone-based developmental diary app (the kleineWeltentdecker App , henceforth referred to as the APP (The German expression “kleine Weltentdecker” can be translated as “young world explorers”.)). By using the APP, caregivers can track changes in their children’s skills during development. Here, we report the construction and validation of the questionnaires embedded in the APP as well as the technical details. Empirical validations with children of different age groups confirmed the robustness of the different measures implemented in the APP. In addition, we report preliminary findings, for example, on children’s communicative development by using existing APP data. This substantiates the validity of the assessment. With the APP, we put a portable tool for the longitudinal documentation of individual children’s development in every caregiver’s pocket, worldwide.

Full text

Behavior Research Methods
https://doi.org/10.3758/s13428-021-01755-7
The kleineWeltentdecker App - A smartphone-based
developmental diary
Moritz M. Daum1·Marco Bleiker1·Stephanie Wermelinger1·Ira Kurthen1·Laura Maffongelli2·
Katharina Antognini3·Miriam Beisert1·Anja Gampe1,4
Accepted: 18 November 2021
©The Author(s) 2022
Abstract
Today, a vast number of tools exist to measure development in early childhood in a variety of domains such as cognition,
language, or motor, cognition. These tools vary in different aspects. Either children are examined by a trained experimenter,
or caregivers fill out questionnaires. The tools are applied in the controlled setting of a laboratory or in the children’s
natural environment. While these tools provide a detailed picture of the current state of children’s development, they are
at the same time subject to several constraints. Furthermore, the measurement of an individual child’s change of different
skills over time requires not only one measurement but high-density longitudinal assessments. These assessments are time-
consuming, and the breadth of developmental domains assessed remains limited. In this paper, we present a novel tool to
assess the development of skills in different domains, a smartphone-based developmental diary app (the kleineWeltentdecker
App, henceforth referred to as the APP (The German expression “kleine Weltentdecker” can be translated as “young world
explorers”.)). By using the APP, caregivers can track changes in their children’s skills during development. Here, we report
the construction and validation of the questionnaires embedded in the APP as well as the technical details. Empirical
validations with children of different age groups confirmed the robustness of the different measures implemented in the
APP. In addition, we report preliminary findings, for example, on children’s communicative development by using existing
APP data. This substantiates the validity of the assessment. With the APP, we put a portable tool for the longitudinal
documentation of individual children’s development in every caregiver’s pocket, worldwide.
Keywords Ambulatory assessment ·Experience sampling ·Longitudinal research design ·Smartphone application
Challenges in the longitudinal assessment
of development
The measurement of developmental change is challenging.
Our current knowledge about children’s development results
to a large extent from cross-sectional studies. Mostly, different
Moritz M. Daum
moritz.daum@uzh.ch
1Department of Psychology and Jacobs Center for Productive
Youth Development, Developmental Psychology: Infancy and
Childhood, University of Zurich, Binzmuehlestrasse 14, Box
21, CH-8050 Zurich, Switzerland
2Johannes Gutenberg University Mainz, Mainz, Germany
3University of Applied Sciences in Special Needs Education,
Zurich, Switzerland
4University of Duisburg-Essen, Duisburg, Germany
individuals of different ages are tested within a narrow
time window. This approach is vital for the assessment
of age differences, but it only provides a static picture of
current developmental states. As a result, a large amount
of research in developmental psychology is dedicated to
the description of children’s behavior at different ages. It
has therefore become somewhat conventional to describe
the earliest manifestations of particular abilities (Adolph
et al., 2008). However, already Vygotsky raised the concern
that a cross-sectional approach primarily focuses on age-
dependent and stable endpoints in development (Vygotsky,
1978). Similarly, Adolph and colleagues stated that this
kind of research has resulted in “a gallery of before and
after snapshots, studio portraits of newborns, and fossilized
milestones” (Adolph et al., 2008, p. 527). With these
static developmental pictures, little can be learned about
developmental processes.
To a certain degree, this shortcoming is compensated
for by longitudinal research paradigms. Here, the same

Behavior Research Methods
individuals are tested multiple times at predefined measure-
ment points, for example, every month or every year. This
approach provides information about individual develop-
mental trajectories by relating early and later developing
skills. However, this assumed “gold standard” approach has
likewise disadvantages. First, it remains unknown what hap-
pens between the different measurement points. According
to Adolph and colleagues, “sampling rates typically used by
developmental researchers may be inadequate to accurately
depict patterns of variability and the shape of developmen-
tal change” (Adolph et al., 2008, p. 527). That is, when the
sampling rate chosen is too low, it does not allow to identify
whether a developmental trajectory reflects a smooth and
monotonic improvement, a non-linear trend, or an accel-
erating or decelerating transformation. Second, even on a
small scale, longitudinal studies are often highly resource-
intensive. They require an extensive amount of human and
financial resources, and often a substantial amount of time.
Third, the measurement points are usually determined based
on the mean age at which certain developmental milestones
are expected to be reached. This limits the validity of stan-
dard longitudinal research paradigms because the assumed
mean age and the accordingly determined measurement
point do not necessarily reflect a single individual’s devel-
opment (Hamaker, 2012). In addition, the actual moment in
which a developmental change occurs is not captured with
predefined measurement points.
To overcome this limitation, we present a new
smartphone-based developmental diary approach that
adapts the Age-of-Attainment (AoA) method (e.g., Eaton
et al., 2014). The AoA method has its roots in event-
centered approaches (e.g., Campbell & Weech, 1941;
Wohlwill, 1973). It does not measure developmental pro-
cesses by the presence or absence of a developmental mile-
stone, for example, whether or not a 12-month-old already
walks independently. Rather, it helps to identify the point in
time of the emergence of the skill. This allows researchers
to capture the individual age differences at which children
reach a specific developmental milestone. As a result, the
AoA method helps to shift age from being a predictor of
other variables to being the outcome explained by those
other variables (Wohlwill, 1973). The age at which children
first reach a specific developmental milestone (e.g., inde-
pendent walking) shows substantial inter-individual vari-
ability. Capturing this variability may reveal information
about underlying developmental processes, for instance by
informing about how skills acquired early relate to later ones
(Bornstein et al., 2013; Dinehart & Manfra, 2013). Deter-
mining the AoA requires behavioral observation that is of
higher frequency than the usual applied yearly or monthly
observations (optimally, a 24/7 tracking of a child’s devel-
opment). For feasibility reasons, this requires outsourc-
ing data collection from the controlled environment of a
laboratory to the home environment of the children and their
caregivers. Current technological developments, such as the
widespread availability of smartphones, have the potential
to overcome the limitations developmental research was fac-
ing so far and to facilitate the collection of comprehensive
AoA data.
Skills do not develop independently of each
other
In the past, various researchers have described how to
implement designs with multiple outcome measures (LoBue
et al., 2020; LoBue & Adolph, 2019; Aslin, 2007; Morris
et al., 2006). With the present APP, we aim to expand
this view by focusing on the second methodological and
theoretical challenge of developmental research: Skills
do not develop in isolation. Neither do they develop
independently from each other nor independently of the
environmental context, which also changes at the micro,
meso, and macro levels (Bronfenbrenner, 1992). On the
contrary, when a particular skill in one domain occurs
or changes, skills in other domains often do not remain
unaffected (e.g., Smith & Thelen, 2003).
Let’s exemplify this with the development of basic
motor skills: Motor development results from the co-
occurrence and interactions of basic maturation processes
such as the increased myelinization of the cortical-spinal
tract (McGraw, 1943; Zelazo, 1998), other physiological
systems (muscle strength and the ability to balance Spencer
et al., 2000; Adolph et al., 2003), cognitive and perceptual
skills, social-emotional change (e.g., the motivation to
move independently), experience (adequate opportunities to
practice the emerging skill), which are often influenced by
cultural and historical differences in child-rearing practices
(Adolph & Hoch, 2019). Vice versa, the development
of motor skills is strongly influenced and refined by
perceptual, cognitive, motivational skills as well as by
cultural and historical differences in child-rearing practices
(Adolph & Hoch, 2019). And vice versag the acquisition
of new motor skills lays the cornerstone for the emergence
and refinement of skills in other domains (Soska et al.,
2015; for overviews, see Campos et al., 2000 and Gredeb¨
ack
et al., 2021). For example, changes in locomotion result in
changes in perception: Crawling infants’ look down at the
floor to a great extent. In contrast, walking infants direct
their gaze at their caregivers and objects in the environment
(Kretch et al., 2014). Furthermore, locomotion influences
infants’ cognitive skills (Campos et al., 2000), such as their
mental rotation of objects: Crawling infants show better
mental rotation than non-crawling infants (Schwarzer et al.,
2013). Mental rotation is further positively influenced by
the infants’ general motor experience (Frick & Wang, 2013;

Behavior Research Methods
M¨
ohring & Frick, 2013). Also, fine-motor skills (Dinehart
& Manfra, 2013) and early action experiences (Bornstein
et al., 2013) are significantly related to later academic
achievement. Concerning the cultural and historical context,
it has been shown that the position in which children sleep
(supine or prone) has an impact on the age of acquisition
of several motor milestones. Compared to supine sleepers,
prone sleepers start earlier rolling prone to supine, tripod
sitting, creeping, crawling, and pulling to stand (Davis et al.,
1998). The American Academy of Pediatrics recommended
in 1992 that infants should be placed on their side or
back for sleep (Pediatrics, 1992) to reduce the incidence
of sudden infant death syndrome. With this intervention,
the percentage of infants sleeping prone has decreased
and, accordingly, the age when different motor skills are
acquired has increased. This shows how the context in
which children grow up provides different opportunities
resulting in different developmental trajectories.
While knowledge about specific interrelations such
as the ones just reported is increasing, the assessment
of the development of the interrelations between skills
in different domains and, in particular, their dynamic
interaction over time remains limited. The developmental
diary approach presented here implements the following
features: It includes the development of skills in different
domains (cognition, language, motor, and social and
emotional skills). The temporal assessment is shorter
compared to a large number of longitudinal studies. It
relates the development of these different domains to each
other. Finally, it considers contextual factors such as the
language(s) spoken by the child and the caregivers, and the
caregivers’ cultural, educational, and economic background.
Goals of the kleineWeltentdecker App (APP)
To address these challenges, we developed the kleineWel-
tentdecker App (henceforth referred to as the APP), a
smartphone-based digital developmental diary application.
With the APP, we provide a tool to the caregivers to doc-
ument the development of their children from age 0 to 6.
At the same time, caregivers share the data of their chil-
dren’s development anonymously with our research unit
(see also “Data security and data protection”). With this par-
ticipatory science approach (“caregiver-as-a-researcher”),
the APP allows acquiring and analyzing longitudinal data at
a relatively high temporal resolution optimally at the exact
moment when a developmental change occurs. The follow-
ing three goals drove the development of this research tool
and its related research.
Goal 1: Establish a comprehensive data set of child
development from age 0 to 6, within and across
individuals
Given the ubiquity of smartphones worldwide, the range
of use of the APP is not limited to specific regions or
countries. This aspect facilitates the analysis of the variabil-
ity of behavior and its development regarding contextual
aspects such as culture, SES, language background, and
many other demographic and family factors. The acquired
data are therefore subject to analyses for the following
major purposes: 1) It allows an in-depth analysis of the
individual developmental trajectories of major develop-
mental domains. 2) It allows analysis of the dynamically
changing interrelations and inter-dependencies of the devel-
opment in the individual domains. With this approach,
developmental trajectories can be compared within and
between individuals, or within and between cultures, which
helps to identify developmental specificities and universals.
Goal 2: Account for the variability on development
across cultures
One issue of increasing importance in developmental sci-
ence is the variability in children’s development. Previous
research on psychology in general and in developmental
psychology in particular is based on data from WEIRD
(Western, Educated, Industrialized, Rich, and Democratic)
populations (e.g., Nielsen et al., 2017). There is a grow-
ing number or researchers who argue that this approach
undermines the variability of behavior and development
across the globe. Henrich et al. (2010b) state that their
“findings suggest that members of WEIRD societies,
including young children, are among the least representative
populations one could find for generalising about humans”
(p. 61). A bias towards WEIRD samples may result in
that findings, which are specific to a particular culture, are
falsely being interpreted as universal traits (Henrich et al.,
2010a; Nielsen et al., 2017). Accordingly, the second goal of
the APP is to provide a tool that is not (or at least much less)
restricted to the collection of data within a narrow range
of participants but is - optimally - available worldwide.
In a first step, we implemented the APP in four different
languages: British English, French, German, and Italian.
We are aware that, currently, the APP asks caregivers
about their children’s skills in a fixed and to some
extent “WEIRD”-based order and children from different
cultures may develop in a different order. However, the
current approach will help to identify commonalities and
differences between cultures and will be helpful to identify

Behavior Research Methods
developmental sequences that differ from norms based on
WEIRD societies.
Goal 3: Outsourcing of data collection
Collecting longitudinal data from different domains and
from children aged 0 to 6 requires an enormous effort
and is resource-intensive. With the APP, data collection is
outsourced to the caregivers of the child. This approach
does not come without challenges, which will be addressed
in greater detail in the “Goals and challenges”inthe
Discussion below. Caregivers experience their children’s
behavior in more instances and more varied situations than
a laboratory setting can establish. These different contexts
might support the observation of the emergence a new skill.
Several features were implemented to facilitate caregiver
evaluation: Caregivers receive packages of questions that fit
the child’s current age range in which the developmental
steps usually occur. The questions are enriched with
information about possible variations of the observable
behavior to facilitate answering the questions. Besides this
standard procedure, it is of course possible to answer
questions that are not in these packages. Like this, the
predefined selection of questions, which is based on the
mean age of development, does not restrict answering
questions that are outside of this age window. Further, the
questions are complemented with additional information
about the particular behavior and how it is integrated into
children’s development from a broader perspective. This
helps caregivers to evaluate whether or not their child
already shows a certain skill or not.
To sum up, with the APP, caregivers are provided
with a scientifically substantiated tool to document the
development of their children between birth and the age
of 6 years. It is designed to be intuitive and easy to
use to facilitate continued and sustained documentation
of development. The developmental steps and milestones
are scientifically corroborated and have been tested for
reliability by comparing them to standard instruments (see
“Psychometric properties of scale”, below).
In the following sections, we describe the different APP
scales in more detail. We will explain the major participant
target group of the APP, the construction of the different
scales, ethics and data security, technical specifications, and
provide details on the psychometric properties of the APP.
Scale protocol
Participants: Target group
The target group of the APP are caregivers of children
between 0 and 6 years. We conducted a survey among
799 Swiss caregivers who already participated with their
children in one or more studies of our research unit. The
results showed that >85% of the caregivers would like
to use a digital developmental diary app and >95% of
these would agree to share the data with the research unit.
In general, caregivers seem to be open-minded to modern
media and a substantial number of caregivers is willing to
use the APP and share the collected data increasing the
potential for acquiring data from a sample large enough to
make reliable conclusions.
Content: Domains and items
The questions implemented in the APP target the main
domains in early childhood development: cognitive, lan-
guage, motor, and social-emotional skills. To obtain a com-
prehensive picture about the context of each individual
child’s development, questions about caregiver education,
country of birth, family constellation, language exposure at
home and in childcare, etc., are included. An overview of
all questions is available in the Open Science Framework
(OSF; https://osf.io/ar7xp/).
Construction of items
To be included in the APP, items had to fulfill two
main characteristics: On the one hand, developmental skills
assessed within the APP need to be scientifically relevant.
That is, the skills have been documented in scientific
papers on infant and child development or are included
in diagnostic tools to assess the development of the skills
of a child at a given age. On the other hand, the APP
has to account for the fact that the questions are not
answered by trained experts but by caregivers who might not
be familiar with the jargon of developmental psychology.
Accordingly, the assessment of skills needs to be tailored
in a way that it can easily yet still reliably be performed
by the caregivers, independent of their language skills and
educational background. That is, for all scales, the items
were formulated so that they (a) are easy to understand
and imply face validity, (b) refer to the child’s observable
behavior and do not require implicit measurements, (c)
can be clearly, objectively, and reliably answered by
the caregivers’ observation alone (avoiding sophisticated
measurement techniques), (d) refer to materials which can
be found in a usual household, and (e) still ensure scientific
precision.
The construction procedure included the following three
steps: 1) We started with a comprehensive literature search
collecting skills that typically develop in the first 6 years
of life in the domains of cognitive, language, motor,
and social-emotional development. 2) All skills identified
were evaluated with regard to whether it was possible to

Behavior Research Methods
formulate a question and corresponding answer options that
are scientifically relevant, precise, and unambiguous and at
the same time feasible and understandable for laypeople.
This initial collection of potential items comprised 34
items on cognitive development, 194 items on motor
development, and 245 items on language development.
3) From these preliminary items, we created a first set
of ‘pre’-questionnaires and asked caregivers of children
between 3 and 78 months (n=1397; ngirls =657, nboys =
739, nother =1, Mage =464 days, SE =526 days) to
fill them out. Caregivers additionally provided feedback on
whether a particular item was easy or difficult to assess or
ambiguous in its formulation. Based on this feedback, 17
items were excluded from the final APP scale. Including
the items of the social-emotional scale that were adapted
from existing scales (e.g., the Infant Behavior Questionnaire
- Revised (IBQ-R), Gartstein & Rothbart, 2003, see section
“Social-Emotional Scale”, below), this process resulted in
a total number of 630 items, see Table 1. The particular
construction of the items in the four domains (Scales)is
described in more detail in the following sections.
Cognitive scale
The items of the cognitive scale are grouped according to the
following constructs: sensori-motor development, problem-
solving, and numerical and categorical knowledge. The 19
sensori-motor items include questions on children’s object
exploration and manipulation, reaching, attention, pointing,
imitation, and pretend play. The nine problem-solving
items assess the children’s object permanence, means-end
behavior, memory, and mastery of new problems. The
six items on numerical and categorical knowledge include
questions on children’s counting abilities, color-naming
skills, and knowledge about object sizes and physical laws.
For item construction, the cognitive scales of existing
instruments such as Bayley Scales of Infant Development
(Bayley, 1993), the Intelligence and Development Scales
- Preschool (Grob et al., 2013, IDS-P;), and the Griffiths
Scales of Childhood Development (Green et al., 2016)were
screened and served as a basis for item selection. One
Tab le 1 Number of items per domain that were included in the final
version of the APP
Domain Number of items
Cognition 34
Language skills (Syntax, Grammar) 157
Motor 176
Social / Emotional 151
Demographics 24
Total 630
item was created by the authors. It describes a behavior
that is commonly observed by caregivers and considered
as a milestone in development but was not found in any
developmental scale (CG34: “Can your child tie his/her own
shoelaces?”). Each item sketches a concrete behavior or
instructs to provoke a certain behavior. For details on answer
options and examples of items, see Appendix.
Language scale
The following skills were implemented in the language
scale: early pre-verbal, morphological, and syntactical skills
as well as pragmatic skills. The 16 early pre-verbal skills
include cooing, babbling, and the production of gestures
such as pointing. The morphology scale consists of 23
items. It includes the flexion of adjectives, nouns for plural,
and verbs for past and present tense. It further includes
fusion of articles and pronouns or prepositions. The syntax
scale comprises 65 items on the combination of clauses
using conjunctions and relative clauses, Wh-questions,
indirect speech and conditionals. To assess pragmatics
skills, we implemented the Orion’s Pragmatic Language
Skills Questionnaire (e.g., Ghahari et al., 2017), which
assesses nonverbal communication, language production,
conversational skills like topic maintenance and turn
taking, speech conventions, and peer skills in 53 items.
For all morphological and syntactical skills, we created
prototypical sentences in which the target morphological
flexion or syntactic construction word were highlighted.
The sentences include every-day topics like caregivers
working, children visiting playgrounds, reading books, etc.
The words used in the prototypical sentences to express
these topics are all early acquired (in the first 2–3 years)
by children as cross-validated with the MacArthur Bates
Communicative Developmental Inventories (Fenson et al.,
2007). For details on answer options and examples of items,
see Appendix.
Motor scale
The motor scale includes fine- and gross-motor skills. The
78 fine-motor items include visual-motor integration, grasp-
ing, and graphomotorics. The 98 gross-motor items include
stationary motor skills, locomotion, and object manipula-
tion. Item construction was geared towards existing scales
such as the Peabody Developmental Motor Scales: Second
Edition (Folio & Fewell, 2000,PDMS-2;)ortheBailey
Scales of Infant Development: Second Edition (Bayley,
1993, BSID-ii;). Scales were screened and served as a basis
for the decision regarding which items to include in the
diary. For all identified motor skills, we created items that
describe important motor milestones. For details on answer
options and examples of items, see Appendix.

Behavior Research Methods
Social-Emotional Scale
The social-emotional scale includes measures of infants’
and children’s temperament and attention as well as their
Theory of Mind (ToM). Child temperament is considered
stable over time and a personality trait (Goldsmith &
Campos, 1982; Rothbart, 1981; Zwickel, 2009; Thomas &
Chess, 1977). Therefore, it is assumed that temperamental
characteristics remain relatively stable within and across
the first years of life (Bornstein et al., 2019; Carnicero
et al., 2000; Pedlow et al., 1993; Peters-Martin & Wachs,
1984; Rothbart et al., 2000; Rubin et al., 2002). Therefore,
unlike the items of the other scales, the items in the social-
emotional scale are only asked at one point in time per scale
and do not follow the AoA approach. Because ToM is often
considered as not being stable, in the next version of the
APP, repeated presentations of particular questionnaires will
be implemented.
To assess the children’s social-emotional development,
we included four scales measuring attention, early temper-
ament, and social-cognitive development between the ages
of 3 months and 6 years: 1) The Infant Behavior Ques-
tionnaire for infants aged 3 to 12 months (Gartstein &
Rothbart, 2003, IBQ-R,). 2) The Early Childhood Behav-
ior Questionnaire (Putnam et al., 2006, ECBQ,) for children
between 18 and 36 months (Putnam et al., 2006). 3) The
Children’s Behavior Questionnaire (CBQ) for children 3
years and older (Rothbart et al., 2001) that is suitable for
the age range between 3 and 7 years. 4) The Children’s
Social Understanding Scale (Tahiroglu et al., 2014, CSUS,)
to assess children’s ToM. For details on the measures and
answer options, see Appendix. For detailed information
about the validity, and the reliability, we refer to the original
publications mentioned.
Specifications of the APP
In the following, we first provide information about data
security, storage, and ethical approval, followed by technical
details about the programming structure and set of the APP.
Ethics and data security
Ethics approval and informed consent
The study protocol and the procedures were approved
by the local ethics committee (Reference Number 20.6.5)
and are in accordance with the ethical standards of
the 1964 Helsinki Declaration and its later amendments.
Caregivers are, for example, free to stop using the APP
at any time without giving reasons for justification. All
caregivers who intend to use the APP provide informed
consent. No incentive other than the free use of the APP
is provided to the children and their caregivers by the
research unit Developmental Psychology at the Department
of Psychology and the Jacobs Center for Productive Youth
Development of the University of Zurich (henceforth
referred to as HOST). When registering for the APP, a user
explicitly agrees to the data processing as set out in the
Terms of Use for the APP and the Privacy Policy by the
University of Zurich (UZH). The HOST will continuously
refine the APP. At some instances, this will lead to changes
in the data processing by the HOST. Users will be notified
of such changes in an appropriate manner (e.g., at the next
login).
Data security and data protection
After installation, declarations of consent under data
protection law are obtained and a declaration is made
as to which data are shared with the research unit
“Developmental Psychology” at the UZH and which is
stored locally on the device but not forwarded to the server.
All non-local data are sent via authentication tokens to a
virtual server hosted and maintained by the IT Services of
the UZH. Only UZH staff responsible for the maintenance
of the server, the programmers for update functions, and
authorized staff of the Department of Psychology and
the Jacobs Center for Productive Youth Development at
the UZH have access to the data. The data security
strategy has been approved by the Data Security Office
of the UZH and the Data Security Office of the Canton
of Zurich / Switzerland (https://www.zh.ch/de/politik-staat/
datenschutz.html). The data protection declaration can be
viewed under https://t.uzh.ch/1dA. Cooperating research
units can be granted access to parts of the data if they
sign a data delivery contract with the HOST and when they
have received a declaration of consent from the participating
caregivers. All information about data protection is available
on https://osf.io/jxspz/.
Data shared with the researchers
Data transmitted to the HOST is restricted to the infor-
mation related to the questions asked (see https://osf.io/
ar7xp/). Other data are collected solely within the APP and
not synchronized with the HOST. This includes the e-mail
address of the user, the name of the child, any photo or
video material collected, any individual comments on spe-
cific developmental steps, own entries for personal events.
These data are stored locally and encrypted on the care-
givers’ own mobile device. The HOST has no access to
these data.

Behavior Research Methods
(a) (b) (c)
Fig. 1 Depiction of the APP navigation: (a) Home screen of user navigation, (b) item and answer options, (c) options to indicate the time since
when a child shows a particular skill
Technical specifications
Operating systems
Front end and back end of the APP have been programmed
and are maintained by the companies Hybrid Heroes
GmbH (Berlin, Germany, http://www.hybridheroes.de)and
Smartcode (Z¨urich, Switzerland, http://www.smartcode.ch)
as a hybrid app that works for the operation systems iOS
and Android.
Graphical User Interface (GUI)
The home screen of the APP includes the following sections
(see Fig. 1): 1) Settings: Here, basic settings can be adjusted
such as the frequency and time of push notifications to
inform caregivers that new questions are available for the
APP, username and password, and whether or not the
development of one’s own child shall be compared with
the available norm values. 2) Questions: Caregivers are
provided with the specific questions/items about developing
skills in the four domains. All items and milestones are
illustrated with pictures. The visual appearance of the items
is based on a stack of cards. Each card contains a question
about a particular skill on the front side. Swiping the card to
the left reveals the next card and item. A swiping movement
to the right brings back the previous item. Each card can be
flipped over to reveal the section 3) Knowledge on the back
side that includes information about the skill at question
and its typical development. 4) Diary: For the cognition
and the motor scales, caregivers see the acquired skills of
their children with the corresponding date of attainment 1.
Caregivers who indicated in the Settings that they wished
to compare their child’s data with the available norm data
can access this norm distribution derived from the whole
population of children included in the APP. In the Diary
section, caregivers can also add individual personal events
that are not included in the set of questions (e.g., the
appearance of the first tooth, the first day at the nursery,
birthdays, etc). In this section, caregivers can furthermore
upload pictures to enrich their diary. These individual
personal events and pictures will not be shared with the
HOST (see Ethics and data security). 5) Further options:
Further pages contain core data of the children (date of birth,
sex).
Scientific illustrations
A scientific illustrator (Nadja Stadelmann, http://www.
nadjastadelmann.ch) created illustrations for all items
to visualize the corresponding skills. These illustrations
visualize the domain (demographic information, socio-
emotional skills) or a concrete developmental skill. She
developed illustrations for four children of both sexes in
different ages: at 4, 12, 24, and 48 months. Exemplary
illustrations are shown in Fig. 2. The children are depicted
using a planar style and the caregivers are depicted using a
linear style. This resulted in a strong focus on the child’s
behavior. To illustrate the movements, single movement
1The respective answer options only allowed a systematic depiction of
the AoAs for these two scales

Behavior Research Methods
(a) (b) (c) (d)
Fig. 2 Depiction of the four age groups: (a) An infant at 4 months, (b) an infant at 12 months, (c) a toddler at 24 months, and (d) a preschooler at
48 months
steps are color-highlighted using hue saturation lightness
(see Fig. 3(b)). In some illustrations, the order of steps was
accompanied with coloured arrows or numbers (see Fig. 3
(c)). In the APP, the user can assign one of eight colors
to a child. The illustrations are using this basic color in
combination with the hue saturation color gradation.
Answer options
First, caregivers answer on a dichotomous scale whether
their child has attained the skill (Yes) or not (No). If
caregivers indicate “Yes”, they are further asked to indicate
since when the child mastered the skill. The following
options are available: “since today”, “for a few days”, “for
1–2 weeks”, “for 3–4 weeks”, “for more than 4 weeks”,
“since...(choose exact date)”, see Fig. 1. For some of the
scales (e.g., the language scale, the social-emotional scale),
the answer format deviated from this general procedure,
see Appendix for more information. For each item, the
questions and the answering options are presented in
combination with information about the qualitative criteria
of the item and the represented skill. The wording depends
on the respective items. For the small “experiments” the
items ask whether “my child does x”?. For other skills, the
items ask whether the child is in principle able to do x (e.g.,
“can stand on one leg”) because the child does not always
stand on one leg but might have shown this behavior already.
Procedure
Caregiver information to milestones
For all items in the final APP scale, we created informative
content about the skills. We summarized precursors and the
development around the milestone and provided examples
and contextual information. Furthermore, advice and inputs
are given on how to foster developmental progress and
which training would best fit this developmental phase.
Languages
The APP is currently available in four different languages
(German, French, Italian, British English). This includes
three of the four languages spoken in Switzerland (except
Rhaeto-Romance). Caregivers can choose in which lan-
guage to use the APP. The range of languages can be
expanded at any time; researchers around the globe are
welcome to contact the authors.
(a) (b) (c)
Fig. 3 Exemplary Illustrations: (a) Child in planar style, adult in linear style, (b) movement of a child while moving from sitting to free standing,
(c) additional information depicted by the duration in s

Behavior Research Methods
Prompts and repetition of items
After installation, caregivers are prompted via push notifica-
tions periodically in time intervals between “once a week”
and “once a month” to answer a short set of items about their
children’s development. The time interval can be selected in
the Settings section (see Graphical User Interface (GUI)).
It is possible to answer items at any time. If the caregivers
respond with “No” to a certain item, this question will be
repeated after a period of two weeks. The APP currently
selects the items based on the earliest possible time (age
in days) at which this skill was shown in a child from the
data coming from the norm sample within the app. With this
approach, children who have a comparably early AoA are
not missed. To not miss children who have later AoAs, the
questions are repeated until the caregiver indicate that the
skill has been observed.
Psychometric properties of scale
In the following, information about the psychometric
properties of the APP scales is provided including
objectivity, reliability, construct validity, and criterion
validity. We report the psychometric properties for the
cognition, language, and motor scale (except for the
pragmatic language skills assessed by the Orion’s Pragmatic
Language Skills Questionnaire, Ghahari et al. (2017)). The
psychometric properties of the social-emotional scales are
well-documented in the respective publications mentioned
above.
Participants
In the sample used to assess the psychometric properties,
we included all APP data points provided by the caregivers
until the date of data extraction (11 March 2020). The data
were filtered for outliers and test users using the following
exclusion criteria: 1) children were older than 6 years,
2) caregivers were younger than 20 years or older than
55 years2, 3) caregivers provided a highly unlikely birth
country (e.g., Antarctica), 4) caregivers answered fewer
than ten questions, 5) the AoA of a skill was before the
birth of the respective child3. The original sample consisted
of 5067 children. The application of the filtering criteria
resulted in a final validation sample of 2385 children
2Because there were only very few caregivers outside of this age
window we categorized them as test users.
3In the latest version of the APP, it is no longer possible to indicate an
impossible AoA.
(1112 girls, 1265 boys, and eight children for whom
caregivers chose ‘other’ as indication of sex). The mean age
of the children at the date of data extraction was Mchildr en =
791 days, SEchildr en =11 days. In this validation sample,
the APP was used by 1984 mothers, 294 fathers and 16 other
caregivers, 91 did not answer this question. The mean age of
the APP user at the date of extraction was Muser =36 years,
SEuser =0.08 years.
For construct validation, we invited caregivers
(N=256) who filled out the ‘pre’-questionnaires (see
Participants: Target group) for their children to partic-
ipate in a lab study with their children. We compared
caregivers’ answers in the ‘pre’-questionnaire to their
child’s performance in lab-based standardized tests. The
validation sample for the cognitive scale included 74 chil-
dren (ngirls =36, nboys =38, Mage =734 days,
SE =42 days). validation sample for the motor
scale included 97 children (ngirls =46, nboys =51,
Mage =873 days, SE =63 days). The validation sample
for the language scale included 85 children (ngirls =38,
nboys =47, Mage =1480 days, SE =60 days).
Analyses plan
In the following sections, we describe the different
psychometric properties of the APP scales. To analyze
objectivity and criterion validity, we used different multi-
level logistic regressions predicting either the AoA for the
motor and cognitive items or the language scale index for
language skills by domain (motor or cognition), caregiver
education (mother and father), caregiver age (mother and
father), app user (mother or father), sex of the child, and
pregnancy week in which the child was born (see also
Eq. 1in the Appendix). Details about the specific analyses
are reported in the respective sections below. To measure
construct validity, we predicted children’s performance
in lab-based tests with the answers of caregivers for
the according items in the APP assessed via the ‘pre’-
questionnaires using multi-level regressions for the motor,
cognition and language scale. As a reliability measure, we
assessed the internal consistency by calculating Cronbach’s
αseparately for the different scales and age ranges.
Objectivity
To assess objectivity, we analyzed the influence of the APP
users in our regression on the AoA in the motor, cognitive,
and language scales. That is, we tested whether it made
a difference whether the data were entered by mothers,
fathers, or other users. The results showed that the factor
APP user had no influence on the indicated AoA, see
Tables 2and 3.

Behavior Research Methods
Tab le 2 Psychometric values for the assessment of the objectivity and criterion validity for the motor and cognition items: Type III analysis of
variance table with Satterthwaite’s method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Sig. Level
PregnancyWeek 20943 20943 1 1700.2 7.118 .008 **
Sex 517 259 2 1697.2 0.088 .916
AgeMother 206250 206250 1 1699.9 70.100 <.001 ***
AgeFather 20175 20175 1 1697.5 6.857 .009 **
EducationMother 15525 3105 5 1696.8 1.055 .384
EducationFather 14553 2911 5 1693.0 0.989 .423
APPUser 4803 2401 2 1697.1 0.816 .442
Domain 103 103 1 206.0 0.035 .852
Because there were only few cognitive items, we merged items of the motor and the cognitive items in this model and included domain as a factor.
There was no effect of domain. The model accounted for 98.28% of the variance
Reliability
For all scales, we assessed the internal consistency by
calculating Cronbach’s αseparately for the scales and age
ranges. See Table 4for an overview of the results in the
single scales and age ranges. The results indicate a range
between acceptable (α>.70) and excellent (α>.90)
reliabilities for almost all age ranges for the domains of fine
motor, gross motor, and language. Only the value for fine
motor skills between 12 and 18 months was slightly below
the acceptable value of α=.70. The reliability scores for
the cognition items were less solid and mostly ranged below
α=.60 with the exception of the age range between 3 and
6 months (α=81).
Validity
Construct validity
Each scale was validated for different age groups. We
used the pre-questionnaire (see Participants: Target group)
to assess caregivers’ answers to items of the APP and
compared them to children’s behavior in the corresponding
items of existing scales (see below for details on which
scales were chosen) using logistic regressions. We followed
the procedures and scoring guidelines of the existing scales.
For the motor scale, we tested children’s motor skills
with the motor items of the Bayley Scales for Infant and
Toddler Development III (Bayley, 2005, BSID-III,) up to 42
months and with the Peabody Developmental Motor Scales
(Rhonda Folio & Fewell, 2000, PDMS,) for children older
than 42 months. For the cognitive scale, we used the items
of the cognitive scale of the BSID-III (Bayley, 2005). For
the language scale, we used the “Test zum Satzverstehen
von Kindern” [Test of Sentence Understanding of Children]
(Siegm¨uller et al., 2011, TSVK,) to assess syntactic and
morphological skills and the Peabody Picture Vocabulary
Test (Dunn & Dunn, 2007, PPVT-4,) to assess children’s
vocabulary size.
Multi-level logistic regressions were calculated to predict
children’s motor and cognitive performance in the lab (i.e.,
whether or not children showed the respective behavior
when assessed in the lab) for each item individually by the
answers the caregivers provided in the pre-questionnaire of
Tab le 3 Psychometric values for the assessment of objectivity and criterion validity for the language items: Type III analysis of variance table
with Satterthwaite’s method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F) Sig. Level
AgeDays 472779 472779 1 289.29 582.325 <.001 ***
Pregnancyweek 1589 1589 1 387.45 1.957 .163
Sex 4751 4751 1 410.92 5.852 .016 *
AgeMother 95 95 1 321.50 0.118 .732
AgeFather 362 362 1 315.64 0.446 .505
EducationMother 2053 513 4 321.49 0.632 .640
EducationFather 1350 270 5 336.51 0.333 .893
APPUser 2392 1196 2 310.82 1.473 .231
The model accounted for 65.24% of the variance

Behavior Research Methods
the APP. The regression controlled for children’s age and
the time span between the dates when caregivers answered
the APP question and when their child was tested in the lab.
For the Motor scale, the caregivers’ answers significantly
predicted the children’s lab performance, estimate =
1.671,SE =0.196,z =8.548,p < .001, as well as age,
estimate =0.093,SE =0.027,z =3.476,p < .001.
The model accounted for 51.62% of the variance. This
was not the case in the Cognitive scale, where caregivers’
answers neither predicted children’s performance in the lab,
estimate =0.212,SE =0.427,z =0.496,p =.620, nor
age, estimate =0.019,SE =0.030,z =0.632,p =.527.
For linguistic skills, we calculated the grammar scale
index, summing up the usage frequencies of each item,
that is, how often each item occurred in a child’s language
production. With this grammar scale index, we predicted
the total PPVT score and the TSVK score collected in the
lab. Both the PPVT and the TSVK score were calculated
following the instructions in the corresponding manuals. We
ran linear regressions on the TSVK and the PPVT scores
controlling for the time span between both tests, M=58
days, SD = 27 days. Results showed that the grammar scale
index significantly predicted the TSVK score, estimate =
1.450,SE =0.469,z =3.092,p =.003, and the delay
estimate =−0.065,SE =0.021,z =−2.999,p =
.004. The model accounted for 27.05% of the variance.
Similarly, the grammar scale significantly predicted the
PPVT score, estimate =18.321,SE =7.451,z =
2.459,p =.024. However, we did find no effect of delay,
estimate =−0.496,SE =0.546,z =−0.907,p =
.376. The model accounted for 41.42% of the variance. In
sum, the standardized lab test performances were predicted
by caregivers’ answers to the language scale questions of
the APP, which shows excellent content and predictive
validity.
Criterion validity
We investigated criterion validity by predicting the AoA
outcomes and language scores with factors that typically
effect development. Here, we tested the pregnancy week
a child was born, child’s sex, age and education of father
and mother, and the APP user (i.e., whether father, mother,
or another caregiver provided the data) and entered them
into the model. The results are shown in Table 2(Motor
scale and cognitive scale), and Table 3(Language scale).
For the motor and the cognition scale, we found that AoA
was predicted by the pregnancy week with births in earlier
pregnancy weeks being associated with later AoA, and
effects of caregiver age, with older caregivers showing later
AoA, an effect that was stronger for mothers’ age than
fathers’ age. We found no effects for domain (cognition,
motor), child’s sex, caregiver education, and APP user. For
the language scale, we found that boys were evaluated as
having poorer language skills than girls and language skills
increased with age, see Table 3. In sum, factors that typically
effect development such as pregnancy week or gender also
influenced the scores that were obtained by the caregivers.
We therefore conclude that the APP has sufficient construct
validity.
Our analyses of the psychometric properties of the APP
indicate a sufficient objectivity, reliability, and validity for
the motor and language scales. For the cognition scale,
reliability and validity measures need to be improved
in future versions of the APP by editing, including, or
excluding individual items (see Discussion).
Initial and preliminary findings
To further substantiate the validity of the items used and
the general method of ambulatory assessment based on
a digital developmental diary, we present some initial
findings of what can be measured and whether and how
previously reported findings are replicated. First, we present
some data that describe the sample drawn for the current
purpose (see Descriptive data and demographics,dateof
data extraction: 11 March 2020). Second, we present a
preliminary replication of the relationship of non-verbal
and verbal communication skills (see Communicative skills,
below).
Descriptive data and demographics
Caregiver answered on average 75 questions, ranging
between 10 and 285. The mean duration between regis-
tration and last usage (i.e., the mean length of usage) is
4.32 months, ranging between 1 months and 16 months.
On average, caregivers filled in questions on 2.44 different
days per month, ranging between 0.15 and 16 days. Care-
givers answered questions on average every 21 days (M=
21.43 days, SE =0.31). Per day, caregivers answered on
average 3.01 questions (SE = 0.06) questions in cognitive
development, 12.53 questions (SE =0.22) in motor devel-
opment, 7.06 questions (SE =0.25) in language, 2.89
questions (SE =0.12) in social-emotional development,
3.27 questions on physical measures and 10.7 questions
(SE =0.1) on background variables.
Further, as an example of geographical distribution, we
collected data on the countries of birth and living, see
Table 5. Currently, most of the users live in Switzerland
(54%) and Germany (38%), in total 88 countries of
residence were indicated. Finally, as one example for
demographic variables, caregivers are asked to indicate
how their child was born, either via natural birth or via
Caesarean section. The Swiss Federal Statistical Office

Behavior Research Methods
(Statistik, 2020) reports for the year 2017 that almost one-
third (32.3%) of all newborns (n=85.990) in Switzerland
were be born by Cesarean section. The data collected in
our APP reveal the percentage of 32.4% (n=790 out of
N=2437). The two percentages are almost identical, with
no statistical difference between them, χ2=0.009,p =
.931. While we don’t claim that the data generated by the
unsupervised APP use are representative for the population,
it seems that they approximate population statistics in such
key variables.
Communicative skills
Finally, we present data on a developmental psychological
aspect of the APP data: The development of the interrelation
between non-verbal and verbal communication. Previous
research in laboratory settings reported a longitudinal
relation between joint attention and language development
(Farrant & Zubrick, 2012; Morales et al., 2005). Children
who had low levels of joint attention during infancy were
significantly more likely to have poor receptive vocabulary
around age of 5 (Farrant & Zubrick, 2012). Also, children
with lower scores in pointing at the age of 12 months
(pointing only with open hand but not yet with index finger)
were at risk for language delay 1 year later (L¨uke et al.,
2017). The analysis of a subset of infants (n=198,
ngirls =97, nboys =101) taken from the APP data
indicated that the onset of early joint attention ability (i.e.,
child is looking from an object to caregivers and back)
significantly predicted the age at which infants spoke their
first words, β=0.51,p < .001,F(3,194)=50.28,p <
.001,R
2=0.44. There was no main effect of infants’ sex,
β=−21.81,p =.563, nor an interaction of sex and early
joint attention, β=0.06,p =.495. Infants who showed
joint attention earlier in life also spoke sooner (see Fig. 4).
This is in line with the above-mentioned previous findings
that infants’ developing non-verbal social-cognitive skills
are longitudinally related to their emerging language
skills.
Discussion
In this paper, we present a new tool for the assessment
of children’s development from birth to the age of 6
years. Via the use of a smartphone-based developmental
diary application (the kleineWeltentdecker App, referred
to as the APP), caregivers can track the emergence and
the development of their children’s skills in four major
developmental domains. The empirical validations of the
reliability of the procedures with children of different age
groups have (except for the cognition items) confirmed
the robustness of the different measures implemented in
Fig. 4 Relationship between AoA of Sharing Attention as an indicator
of early joint attention ability and the AoA of First Word as an indicator
of early language skills. Individual data points (blue) and box plots are
illustrated
the APP. In the following, we discuss the psychometric
properties, the goals, and the challenges of the APP.
Psychometric properties
The assessment of the psychometric properties resulted
in an overall positive outcome. The high objectivity of
the data is indicated by the fact that no differences were
found between the AoAs and the usage frequency between
mothers, fathers, and other caregivers, which was the case
in all scales.
Reliability
The assessment of reliability resulted in mixed findings
and the reliability critically depended on the scale tested.
Reliability was excellent for the language scale and good
for the motor scale. There is room for improvement with
respect to the cognition scale, for which the reliability was
generally below acceptability.
There are several potential reasons that can explain
the non-optimal results for the cognition scale. First, the
scale consists of fewer items (n=34) compared to
the other scales (all ns>100), which might result in
a larger variability of the results. Second, the range of
tested skills is relatively broad and thus heterogeneous. It
ranges from simple sensori-motor items to more complex
tasks on memory or problem-solving. Third, and probably
most important, the items often involve the instructions
for caregivers to conduct a little “experiment” with their
children. For example, basic memory functions are assessed
via the following item: “Try this little experiment: Put 3

Behavior Research Methods
pairs of shuffled memory cards picture-side up on a table
in two rows. Ask [Child name] to remember where each
card is. Then, turn over the cards one by one so that
the pictures are no longer visible. Now ask [Child name]
where the paired pictures are, one by one.” (Item CG32).
While the instructions are formulated as easy-to-understand,
caregiver-friendly, and unambiguous as possible, there
is still room for variation in how exactly caregivers
perform these experiments and how the child’s behavior is
interpreted. Also, the relatively high cost for the caregiver to
perform the task (e.g., getting up and searching for memory
cards) might have prompted a positive response even though
the skill had not yet been developed. Other instruments
that assess children’s cognitive development require trained
examiners to perform the assessment. Findings from citizen
science research are helpful to shed more light on this
increase of variability: On the one hand, citizen scientists
can perform collections of valid basic data even when given
only a brief training (Darwall & Dulvy, 1996; Evans et al.,
2005;Foreetal.,2001; Graham et al., 1996). On the other
hand, data validity is decreased when citizen scientists are
confronted with more complex questions and observation
tasks, such as observations in astronomy (Balcom, 2015). In
general, without proper training in experimental protocols,
citizen scientists (such as the caregivers who use the APP
can be compared to) are more likely to introduce variability
into their data (Eaton et al., 2002; Danielsen et al., 2005).
Applied to the present set of cognitive items, it might be the
case that they are in general more difficult to evaluate than
the motor or language items. To conclude, the data collected
with the current cognition items are not yet as reliable as the
other scales. Further developments are required to improve
this scale.
Validity
Our analyses on construct and criterion validity yielded no
effects of caregiver education on the data. This indicates that
caregivers of all educational backgrounds respond similarly
to the questions. Caregiver education is only a rough
estimate for a more global assessment of caregivers’ socio-
economic status (SES). We will, therefore, evaluate whether
a more differentiated assessment of SES (e.g., asking for
income and other aspects) is required and likewise feasible
and accepted by caregivers. The present data showed that
children’s sex had a significant effect on their language
development. Girls generally had earlier language AoAs
than boys. This finding is well established in the field:
Girls produce sounds and use words at an earlier age,
have larger vocabularies, greater grammatical complexity,
and read sooner than boys (Bornstein et al., 2004; Stolt
et al., 2008; Reilly et al., 2019; Miller & Halpern, 2014;
Lisi et al., 2002; Lange et al., 2016). Interestingly, the
AoA was related to the caregiver age. Children of older
caregivers had later AoAs. Previous research reports that
caregiver income related to better problem solving and
language scores (Yeung & Linver, 2002) and that caregiver
job loss had an impact on their children’s performance in
school (e.g., Rege et al., 2011; Stevens & Schaller, 2011),
an effect that seems already visible even before children
enter school (Mari & Keizer, 2020). Caregiver income and
their SES increase with caregiver age (e.g., Featherman
et al., 1988; Mclanahan, 2004;Powelletal.,2006;Ross
& Mirowsky, 1999). Children from older caregivers should
therefore have earlier AoAs than children from younger
caregivers. In light of this tendency, the present data are
in contrast with this previous data. However, the analysis
of the demographics indicated that the general level of
education of the caregivers using the APP was relatively
high and variability was relatively low. Previous research
shows that caregiver education and children’s outcome
are considered at a bivariate level only, the relationship
can be curvilinear and disadvantageous for children with
comparatively young or old caregivers (e.g., Powell et al.,
2006). However, when considering additional factors such
as SES or family structure, the pattern typically becomes
linear and caregiver age becomes positively linked to child
outcomes. These aspects require further attention as the
amount and the quality of the data increase. In general, the
results of the analysis of the psychometric properties are
promising. This is particularly the case for the motor and
language scale whereas the results for the cognition scale
are more heterogeneous.
Goals and challenges
In the Introduction, we formulated three major goals. We
aim 1) to establish a comprehensive data set of child
development, 2) to have tool that accounts for the variability
on development across cultures beyond WEIRD countries,
and 3) to outsource data collection to caregivers. In the
following, we discuss how the APP can help researchers to
reach these goals and the challenges that have yet to be met.
Goal 1: Establish a comprehensive data set of child
development from age 0 to 6, within and across individuals
With the APP, we aim to obtain data that inform
about the variability of behavior and its development in
relation to contextual aspects. The APP measures children’s
competencies in the cognitive, language, motor, and social-
emotional domains of development. Furthermore, questions

Behavior Research Methods
on children’s culture, SES, and language background offer
information on their environment. The analysis of data
acquired by the APP is not limited by time-consuming
processes of manual coding of behavioral data. For the
analysis, it does not make a big difference whether the data
set includes 30 or 30,000 participants. The data of the APP
will provide information that goes far beyond what has been
called the “taking snapshots of developmental outcomes”
approach (Adolph et al., 2008; Caspi et al., 1996), and
has the potential to substantially increase our understanding
of developmental processes. The APP uses an Age-of-
Attainment (AoA) approach (Eaton et al., 2014)thatis
centered on the date of emergence of a developmental skill.
Individuals differ in their AoAs. This particular variability is
of key interest because it provides evidence about how long
it takes individuals to reach a particular skill and to move
to the next skill. That is, it allows evaluating individual
differences in the chronological AoA and the temporal
distances between the AoA of two (or more) different
skills. Eventually, this allows a detailed description of
individual developmental trajectories and the identification
of the interrelations between skills within and across
domains. These descriptions of developmental trajectories
are essential for the advancement of theories about
children’s development and the acquired data will help to
significantly increase our understanding of developmental
change in childhood.
Goal 2: Account for the variability on development across
cultures
Previous research in developmental psychology has to a
large extent been based on WEIRD populations, which
has recently been criticized (e.g., Nielsen et al., 2017).
Therefore, variability of behavior and development is
underestimated. The approach of the APP allows moving
beyond sampling from highly homogeneous (often WEIRD)
populations to a sample of large variability with respect
to cultures and social contexts. Data collected with the
APP allows comparing development within and between
cultures and drawing conclusions from highly diverse
samples. This approach helps to fulfil the plea raised
by, for example, Nielsen and colleagues (2017)thata
“complete understanding of the ontogeny and phylogeny
of the developing human mind depends on sampling
diversity” (p.32), which receives further and increasing
support by numerous other researchers (Clegg & Legare,
2016; Henrich et al., 2010c; Legare & Harris, 2016; Nielsen
& Haun, 2016; van Schaik & Burkart, 2011). This sort
of data as measured by the APP is essential to broaden
our theoretical understanding about which aspects of the
development of skills and traits are universal and which
culture-specific.
Goal 3: Outsourcing of data collection
With the APP, we outsource data collection of longitudi-
nal high-density data to caregivers. With this, we aim to
reduce the enormous personal and financial resources asso-
ciated with such data collection. Moving data collection
from the controlled setting of a laboratory to the “real, noisy
world” and from the hands of trained and experienced exper-
imenters to the caregivers comes with several challenges.
Previous research suggests that, at least for motor and lan-
guage skills, caregiver checklist diaries are concordant with
experimenter home visits (e.g., Bodnarchuk & Eaton, 2004).
The present validation of the motor scales converges with
this finding, but given the not optimal results from the vali-
dation of the cognitive scales, some skepticism regarding the
reliability of caregiver reports might remain. In the follow-
ing, we present three current challenges and the approaches
of how they have been or will be addressed in future refine-
ments of the APP. The solutions might not be final, and
experience and time will provide a more detailed view on
how and how not to address the challenges satisfactorily.
Challenge 1: Infrequent use of the APP
Research designs that use online surveys and smartphone
applications are attractive. They come at relative low cost
and offer great flexibility (LaRose & Tsai, 2014; Barrios
et al., 2011; Evans & Mathur, 2005; Fan & Yan, 2010;
Fricker & Schonlau, 2002; Kaplowitz et al., 2004). At the
same time, they are subject to lower completion rates than
conventional survey methods (B¨
orkan, 2010; Jones & Pitt,
1999; Manfreda et al., 2008; Sax et al., 2003; Shih & Fan,
2008). It is therefore likely that a substantial number of
caregivers will not use the APP regularly. This will result
in a large amount of missing data. There may be ways
that help to increase caregivers’ commitment. Incentives
such as monetary compensation (Frick et al., 1999), loyalty
points (G¨
oritz, 2008), or sweepstakes offering of a certain
monetary value (LaRose & Tsai, 2014) have been shown
to increase commitment in online studies (as indicated by
an increase in response rate to invitations and completion
rate, but see (G¨
oritz, 2006) for null results). For an extensive
overview of psychological and data collection via the
Internet, the reader is referred to the extant literature (e.g.,
Birnbaum, 2004; Manfreda et al., 2008;Reips,2002;Shih
&Fan,2008). However, given the already large number of
participating caregivers (>4.000; March 2020), it is not
feasible to offer any form of monetary incentive to all users.
One potential option is to offer caregivers to participate in
a lottery that takes place periodically where caregivers can
win a voucher that can be used worldwide (e.g., in online
music or book stores). Lotteries seem to have a positive
impact on participant commitment (LaRose & Tsai, 2014).

Behavior Research Methods
Participation in the lottery could be automatized or applied
as an incentive if caregivers contributed a predetermined
number of data points within a given period. A second
option is to implement the APP as a supplementary
measure in a more controlled setting of an existing ongoing
longitudinal study. In such a setting, a smaller number
of caregivers who agreed to take part in a study can
be motivated via incentives more easily and reminded
repeatedly to answer the questions. With this approach,
the usage and data of “unsupervised” caregivers can be
compared to a highly controlled sample of caregivers,
which will provide further insights to the interrelation
between use and data quality. Two examples for this
second option are first, the study Children and Digital
Media (Kinder und digitale medien, 2021, KiDiM;) by the
Marie Meierhofer Institute in Zurich, Switzerland. Here,
APP data complements the collection of longitudinal data
on children’s media use. Second, in a planned study on
the relation between nutrition and cognitive development,
currently prepared by the USZ Neonatology section
(Natalucci et al., 2021, LEARN;), APP data will be used as a
continuous measure of the children’s development between
the specified measurement times. One major aim for the
future will be to use the data coming from “supervised” and
“unsupervised” users to identify different user behavior, its
impact on the data quality, and ways to impute missing data.
High-resolution data from “supervised” users will thereby
serve as a basis to impute missing data of the low-resolution
data of the “unsupervised” users.
Challenge 2: General reliability of questionnaire data
One might in general be skeptical about the reliability
of the data caregivers report. They might be inclined
to answer the questions about the development of their
children too optimistically for reasons of social desirability.
Previous research on the reliability between laboratory
assessments and caregiver reports revealed inconsistent
effects. Whereas caregiver scores often correlate with
professional assessment, they likewise tend to be a poor
predictor of infants at risk of developmental delay (e.g.,
Emond et al., 2005). Caregiver report and experimenter
home visit observations seem to converge when assessing
the development of motor skills. For example, the Parent
Milestone Report Form has been shown to be a reliable
and valid instrument to assess infants’ development of a
number of gross motor skills via caregiver report (Adolph
et al., 2008; Bodnarchuk & Eaton, 2004). Similarly,
Miller and colleagues (2017) showed no differences in the
evaluation of receptive, expressive language, and fine motor
skills between caregiver report and direct assessment via
experts. Further, laboratory tests and experimenter home
visits often underestimate children’s early linguistic abilities
(Bates, 1993) and adults are generally good in estimating
skills. Self-reports, for example in language proficiency
evaluation, converge with objective measures in adults (e.g.,
Marian et al., 2007). Furthermore, the current approach
receives support by the fact that caregivers are with their
children in many more and highly variable situations than
a laboratory setting is able to establish. This makes it more
likely that they observe the skill that the APP asks for and
that caregivers’ assessment of their children is close in time
to the first occurrence of a skill. In contrast, when caregivers
are asked to assess the emergence of their children’s first
words (around the age of 12 months) retrospectively, these
estimates show only weak reliability that decreases with
increasing age of their children (Majnemer & Rosenblatt,
1994,r=0.27 at age 3 and r=−0.11 at age 5,).
The analysis of the APP user objectivity was generally
successful, the results showed that the factor “APP user”
had no influence on the AoA identified. However, one
issue needs to be discussed more critically. While the
reliability was evaluated by two independent observers,
a caregiver via the questionnaire and a researcher in the
controlled laboratory environment, this was not the case
in the objectivity rating, which may have caused selection
effects (e.g., fathers who are more involved in raising
their children might be more inclined using the APP and
might, therefore, be better observers). This is a limitation
for larger conclusions concerning objectivity. However, for
those caregivers who have been using the APP so far, this
does not seem to be a major problem.
In general, while caregivers may be susceptible to social
desirability effects, they are still likely the most reliable
source to determine whether or not a skill is in their child’s
repertoire (Sachse & Suchodoletz, 2008). Caregivers are
in the unique position to observe and interact with their
children across many different situations. This makes them
likely to report the particular moment when a skill was
observed for the first time. The everyday interaction of
caregivers with their children is furthermore not subject
to issues with child motivation and cooperation and has
been established as a valuable way to quickly and cost-
effectively add information important for the detection
of developmental delays (e.g., Nordahl-Hansen et al.,
2014). These aspects result in an increased application of
caregiver reports for routine developmental screening that
are particularly helpful for the identification of children
at risk for developmental delays, a procedure that is in
accordance with the recommendations by the American
Academy of Pediatrics (e.g., Emerson et al., 2016; Johnson
& Myers, 2007).

Behavior Research Methods
With respect to reliability, the current approach repre-
sents a trade-off between feasibility and reliability. Opti-
mally, caregivers would answer the question about the emer-
gence of a particular skill at several instances, repeated over
several days to account for the variability of the emergence
of a particular skill (Adolph et al., 2008). However, work-
load for the caregivers is already substantial in the current
version of the APP with up to 20 questions per notification.
A further increase of this number will result in a signif-
icant decrease of the number of participating caregivers.
The current approach relies on the fact that caregivers are
required to answer the questions about the development of
their children in intervals of 1 week or 1 month. Considering
these circumstances, we are confident the APP a valuable
and important tool that avoids false negatives. Caregivers
observe their children in a variety of different situations and,
triggered by the particular question posed by the APP, might
bring their children in situations in which the new skill
is likely to be observed. It is important to emphasize that
the present scales will not replace any diagnosis of clinical
symptoms or developmental delays, which need an in-depth
diagnosis of an expert psychologist and/or pediatrician.
Finally, to reliably assess the emergence of a developing
skill, measuring daily fluctuations would be optimal.
The APP is not designed to fulfil this purpose but to
assess whether and when a child shows a skill with a
defined quality. This approach is inspired by developmental
assessment tools like the Bailey Scales (Bayley, 2005)and
by the AoA approach (Eaton et al., 2014). The collected
AoAs are snapshots, subject to fluctuations over time.
However, the questions are answered by the caregivers not
on the basis of a single observation at one point in time
but on their everyday observation of their children. The
advantage of this approach is to collect data from a large
number of children in a relatively easy and convenient way
and to be closer in time to the actual AoA of a skill than
traditional approaches.
Challenge 3: Assessment of language skills
While the APP may document children’s development
in the cognitive, motor, and social-emotional domain,
the measures on language development are not yet
comprehensively integrated. The current assessment of
linguistic skills does not yet include vocabulary. In an
initial version of the APP, we included this aspect with
almost 2000 items being asked to caregivers. The feedback
caregivers provided indicated that they became tired quickly
of answering the vocabulary items due to the sheer number
of questions about single words their child might or might
not speak at a given point in time. For this reason, we
removed the vocabulary section in the current version. One
potential approach to this challenge was recently introduced
by Mayor and Mani (2019). These authors presented a new
methodological approach through which an estimation of a
child’s vocabulary score (Fenson et al., 2007, as assessed by
the MacArthur Communicative Development Inventories,
CDI;) can be obtained by combining caregiver responses
on a limited set of words sampled randomly from the full
CDI with the information about how many children do or do
not speak a particular word extracted from the WordBank
database (Frank et al., 2017). The findings show that using a
reduced list of only 25 words provided an accurate estimate
of a child’s vocabulary size for American English, German,
and Norwegian. Implementing an algorithm similar to the
one used by Mayor and Mani (2019) can be one way
to improve the procedure by substantially reducing the
number of vocabulary items. This is a plan for future
implementation.
To sum up the challenges, there are reasons that
caregiver assessment will be both more and less precise
than laboratory assessment. Collecting more data, asking
feedback from participating caregivers, and using this
information to adjust items accordingly will help to increase
reliability in the future. For example, with more data
analyzed, a “real” effect may become visible and the
influence of individual children on the data becomes less
substantial.
Anticipated outcome and significance
Given the ubiquity of smartphones worldwide, smartphone
applications increasingly serve as digital support devices.
With the developmental diary application presented here,
we put a portable data acquisition tool in the pocket of
caregivers. The value of this approach is high (in times
of the SARS-CoV-2 pandemic in which this paper was
partially written, even more so). It includes longitudinal
data of a potentially large-scale, population-based sample.
The sample size is not restricted to areas or by limited
(financial and human) resources. This approach allows to
move beyond sampling from highly homogeneous (often
WEIRD) populations to a sample of great variability
with respect to cultures and social contexts. The data
collected with the APP allow comparing development
within and between cultures and drawing conclusions from
highly diverse samples. Eventually, this allows a detailed
description of individual developmental trajectories, and the
identification of the interrelations between skills within and
across domains.
With the approach presented here, data are collected from
more children in more places at a higher frequency than it is

Behavior Research Methods
possible with moderated testing, either in-person or online.
The descriptions of developmental trajectories derived from
these data are essential for the advancement of theories
about children’s development and the acquired data will
help to increase our understanding of developmental change
in childhood.
Appendix
Details on scales
In the following sections, we describe the answer options of
the cognitive, language, motor, and social-emotional scale
in more detail and give examples of answer options and
items of each scale. All the items of all scales are available
on https://osf.io/ar7xp/.
Cognitive Scale
Answer options Caregivers are asked to indicate whether
or not they observe the behavior in question. We display
answer options in a dichotomous manner. The positive
option is “Yes” and, to be unambiguous, relevant factors of
the respective behavior are additionally outlined concisely
for some items. Hence, the answer options include exact
success criteria which have to be met (e.g., counting
three or more objects). The negative option is “No” and,
where necessary, alternative behavior which would not
justify a positive choice is sketched (e.g., counting less
than three objects). As in the questions for the motor
scale, if caregivers indicate “Yes”, they are asked to
additionally indicate since when the child mastered the skill,
see Fig. 1.
Exemplary items To illustrate, we exemplarily describe one
item for each construct. An early sensori-motor item on
exploration behavior asks: “Does your child play with
or examine his/her fingers?”. Caregivers can answer with
either “Yes, when my child is calmly lying on his/her
back, my child plays with his/her hands or examines
them (turning them around, opening and closing them)”
or “No”. One of the first Problem-solving items assesses
means-end behavior: “Does your child pull at flat objects
(e.g., a cloth or blanket) to grasp a toy that is lying
on it?”. This item has the answer alternatives: “Yes” or
“No, my child moves towards the toy”. A Numerical and
categorical knowledge item investigates children’s counting
abilities: “Can your child count out 10 objects (e.g., building
blocks)?”. Caregivers can answer with “Yes, my child says
“one” to the first object, “two” to the second, and “three” to
the third, etc.” or “No”.
Language Scale
Answer options For each sentence of the morphological
and syntactical items, caregivers are instructed to indicate
how often their child produces such or a similar sentence.
The questions can be answered via a five-point Likert scale
(never; less than once a week; at least once a week, but
not every day; once or twice a day; more than twice a
day) similar to the Children’s Communication Checklist – 2
(Bishop, 2003, CCC2,).
Exemplary items To illustrate, we provide some examples
for items and prototypical sentences. An item for Gestures
asks for: “Does [Child’s Name] point at objects that [Child’s
Name] wants or would like to show you?”. An item for
Morphology assesses one of the plural forms in Swiss
German like this: “Holsch ¨usi Jackene?” (Engl. “Can you
bring our jackets?”). An item for Syntax conjunctions
includes the following sentence: “W¨
ammer wiiterspile oder
wetsch es Buech aluege?” (Engl. “Do we want to keep
playing or should we look at a book?”).
Motor scale
Answer options For each item a dichotomous scale is used
with which the caregivers are asked to indicate whether their
child has attained the skill (Yes) or not (No). If caregivers
indicate “Yes”, they are further asked to indicate since when
the child mastered the skill, see Fig. 1.
Exemplary items To illustrate, we provide some examples
for typical items. Visual-motor integration items assess eye-
hand coordination with objects: “My child can stack 5
building blocks” and without objects: “My child can clap
his/her hands”. Specific Grasping items assess the way
objects are grasped and held how a child uses his/her hands
for grasping: “My child can grasp a rattle, lying on his/her
back”. Graphomotorics items assess how the child uses
pencils to draw and write: “My child imitates me drawing
a line”. Stationary motor skills items assess body posture:
“When I place my child on his/her tummy and shake a rattle,
he/she rotates his head to locate the rattle”. Locomotion
items assess movement in space: “My child can roll from
back to tummy”. Object manipulation items assess the
manipulation of a ball: “When I sit on the floor with my
child and roll a ball towards him/her, he/she will roll the ball
back to me”.
Social-emotional Scale
Details on questionnaires In the following sections,
we provide additional information of the established

Behavior Research Methods
questionnaires, which are used to assess children’s
social-emotional development in the APP.
Infant Behavior Questionnaire - Revised (IBQ-R) The origi-
nal IBQ was developed by Rothbart (1981) and revised as
IBQ-R by Gartstein and Rothbart (2003). The full version
of the IBQ-R includes 14 scales and 191 items. For fea-
sibility and efficiency reasons, we implemented the Very
Short Form version (Putnam et al., 2014, IBQ-R-VSF,) that
includes 37 items in three broad scales (Positive Affectiv-
ity/Surgency, Negative Emotionality, Orienting/Regulatory
Capacity). For the non-English versions, we used the avail-
able translations (German: (Kristen et al., 2007) and slightly
revised by Vonderlin et al. (2012); Italian: (Montirosso et al.,
2011); French: (Cascales, 2011)).
Early Childhood Behavior Questionnaire (ECBQ) The ECBQ
was developed by Putnam et al. (2006). The original ques-
tionnaire includes 18 scales and 201 items. We used the
very short form version (Putnam et al., 2010) that includes
36 items in three scales (similar to the IBQ-R-VSF: Pos-
itive Affectivity/Surgency, Negative Emotionality, Orient-
ing/Regulatory Capacity). For the non-English versions,
we used the available translations (German: (Kirchhoff &
Fuchs, n.d.); French: (Goupil, n.d.); Italian: (Cozzi et al.,
2013)).
Children’s Behavior Questionnaire (CBQ) The CBQ was
developed by Rothbart et al. (2001). The original ques-
tionnaire includes 15 scales and 195 items. We used the
very short form version (Putnam & Rothbart, 2006)that
includes 36 items and three scales (similar as in the IBQ-R-
VSF: Surgency, Negative Affect, and Effortful Control). For
the non-English versions, we used the available translations
(German: (Koglin & Petermann, 2007; Nikolaizig, 2007);
French: (Lafortune et al., n.d.); Italian: (Matricardi, n.d.)).
Children’s Social Understanding Scale (CSUS) Children’s
ToM is usually measured via a range of laboratory
paradigms that assess the understanding of mental states
such as beliefs, desires, emotions, and intentions (e.g.,
Wellman, 2007). We implemented the CSUS (Tahiroglu
et al., 2014), a caregiver-report ToM measure. It includes
six scales (belief, knowledge, perception, desire, intention,
emotion) with a total of 42 items which have been shown
to be a measure of individual differences in children’s ToM
with very high internal consistency, test–retest reliability,
and predictive validity.
Answer options The items used in the different scales
describe behavior that frequently occur in everyday contexts
(e.g., “When being dressed or undressed,”, “When playing
outdoors”, “When told no”). Accordingly, caregivers are
asked to indicate how often their child showed a particular
behavior during the last week (i.e., the past 7 days) on
a seven-point Likert-style format ranging from “never” to
“always”. We applied the same scales as used in the original
questionnaires.
Equation used for analyses of psychometric
properties
To analyze the objectivity and criterion validity of the APP,
we used different multi-level logistic regressions predicting
either the AoA for the motor and cognitive items or the
language scale index for language skills by domain (motor
or cognition), caregiver education (mother and father),
caregiver age (mother and father), app user (mother or
father), sex of the child, and pregnancy week in which the
child was born (1).
AoA/Language Index ∼
Domain +EducationFather +EducationMother +
AgeFather +AgeMother +
APPUser +SexChild +PregnancyWeekChild +(1—Item) (1)
Tab le 4 Internal consistency (Cronbach’s α) for cognition, language,
fine motor, and gross motor items for different age ranges
Scale Age Range (Months) α
Cognition 3 - 6 0.81
Cognition 6 - 12 0.449
Cognition 12 - 18 0.424
Cognition 18 - 24 0.421
Cognition 24 - 36 0.575
Cognition 36 - 48 0.334
Language 24 - 36 0.985
Language 36 - 48 0.982
Language 48 - 72 0.982
Fine Motor 3 - 6 0.918
Fine Motor 6 - 12 0.831
Fine Motor 12 - 18 0.653
Fine Motor 18 - 30 0.742
Fine Motor 30 - 44 0.817
Fine Motor 44 - 72 0.835
Gross Motor 3 - 6 0.9
Gross Motor 6 - 12 0.889
Gross Motor 12 - 18 0.738
Gross Motor 18 - 30 0.748
Gross Motor 30 - 44 0.755
Gross Motor 44 - 72 0.812

Behavior Research Methods
Tab le 5 Number of users (total, mothers, and fathers) of the 15
countries with the largest numbers of users
Country Total n(Mot hers ) n(F at her s)
Switzerland 2472 1227 1245
Germany 1827 918 909
Austria 68 35 33
Italy 52 31 21
Poland 35 12 23
United States 23 12 11
Turkey 22 14 8
Russia 21 9 12
United Kingdom 18 15 3
Romania 18 8 12
Spain 18 12 6
Bosnia 17 6 11
France 15 9 6
Kazakhstan 15 2 13
Kosovo 13 7 6
Acknowledgements We express our deep gratitude to a large number
of people who were substantially involved in the development of
the APP: Rico Leuthold (http://www.smartcode.ch) for programming
thebackendoftheAPPandJanGerwinandChristianJustus
(http://www.hybridheroes.de) for programming the frontend, Christof
T¨
aschler (http://www.christoftaeschler.ch) for the UI/UX design, and
Nadja Stadelmann (http://www.nadjastadelmann.ch) for the scientific
illustrations. Andrea Gr¨
ossbauer, Eva Pouwer, and Robert Weniger
for their support in data protection matters, Nadia Steiner for her
support with legal questions, Ingo J¨
orissen, Jacqueline Martinelli,
Stefan Mischke, and Frank Schleich for their support with the UZH
servers, and Wolfgang Henggeler and Patrick Sticher for the trademark
registration. Ramona Abrecht, Ingo Besserdich, Leonie Hartmann, and
Christina Tschan inserted the questions and answers and worked on
the knowledge texts, Sonja Brunschweiler has proofread them. Jonas
G¨
ahwiler, Maximilian Haas, Natascha Helbling, Mirella Manfredi,
Matthew Rockey, and Virginie Rusca for various translation work.
Nicole Besson, Alexandra Ritter, Rebekka R¨uesch, Isabella Schwyzer,
and Nicole Zahnd have validated the answers given by the caregivers
by testing children in our lab. Julia Brehm, Kerstin Clausen, Julia
Fenkl, Ebru Ger, Sarah Hauser, Karin Hollermayer, Liridona Hoti,
Lara Keller, Lea M¨
orsdorf, Petra Moser, Fiona Pugin, Meret Roth,
Larissa Stuber, and Freya Zacher for testing the APP again and again
and providing useful inputs. Finally, Lisa Wagner and Sabrina Beck for
commenting on a previous version of this manuscript. Without the help
of all these people involved, this project would not have been possible.
Funding Open access funding provided by University of Zurich.
The project was supported by the Department of Psychology (http://
www.psychologie.uzh.ch) and the Jacobs Center for Productive Youth
Development (http://www.jacobscenter.uzh.ch) of the University of
Zurich.
Author Contributions M. M. Daum, K. Antognini, M. Beisert,
M. Bleiker, A. Gampe, I. Kurthen, L. Maffongelli, and S. Wermelinger
jointly generated the idea for the APP. All authors contributed to the
development of the different scales, A. Gampe and S. Wermelinger
wrote the analysis code and analyzed the data, M. M. Daum wrote
the first draft of the manuscript, and all authors critically edited it. All
authors approved the final submitted version of the manuscript.
Declarations
Ethics approval The study protocol and the procedures were approved
by the local ethics committee (Reference Number 20.6.5) and are in
accordance with the ethical standards of the 1964 Helsinki Declaration
and its later amendments. Caregivers are, for example, free to stop
using the APP at any time without giving reasons for justification.
All caregivers who intend to use the APP provide informed consent.
No incentive other than the free use of the APP is provided to the
children and their caregivers by the Research Unit Developmental
Psychology at the Department of Psychology and the Jacobs Center for
Productive Youth Development of the University of Zurich (HOST).
When registering for the APP, a user explicitly agrees to the data
processing as set out in the Terms of Use for the APP and the Privacy
Policy by the University of Zurich. The HOST will continuously refine
the APP. At some instances, this will lead to changes in the data
processing by the HOST. Users will be notified of such changes in an
appropriate manner (e.g., at the next login).
Conflict of Interests The authors declare that there were no conflicts of
interest with respect to the authorship or the publication of this article.
Open Practices Statement The items of all the scales and information
about data protection are available on https://osf.io/ar7xp/. The data
sets generated during and/or analyzed during the current study are not
publicly available due to the current data protection guidelines, see
section Data security and data protection but are available from the
corresponding author on reasonable request.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons licence, and indicate
if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright
holder. To view a copy of this licence, visit http://creativecommons.
org/licenses/by/4.0/.
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