A Playful Smartphone-based Self-regulation Training for the Prevention and Treatment of Child and Adolescent Obesity: Technical Feasibility and Perceptions of Young Patients

Abstract

Effective interventions for the prevention and treatment of child and adolescent obesity play an important role in reducing the global health and economic burden of non-communicable diseases. Although multi-component interventions targeting various health behaviors are deemed promising, evidence for their effectiveness is still limited. Self-regulation seems to be a relevant working mechanism in this regard. Therefore, we propose a playful, smartphone-based self-regulation training that also utilizes the health benefits of a slow-paced breathing exercise. The mobile app uses the microphone of the smartphone to detect breathing sounds (e.g. inhalation, exhalation) and translates these sounds into a visual biofeedback on the smartphone screen. The design and evaluation of a very first prototype is described in this interdisciplinary work of obesity experts, clinical psychologists, young patients, and computer scientists. The apps' breathing detection module uses a random forest tree for quasi real-time classification of the incoming audio samples and biofeedback generation. A study with 11 obese children and adolescents was conducted to assess the prototype. Results indicate overall positive evaluations and suggestions for improvement. Implications and limitations are discussed, and an outlook on future work is provided.

Full text

A Playful Smartphone-based Self-regulation Training for
the Prevention and Treatment of Child and Adolescent
Obesity: Technical Feasibility and Perceptions of Young
Patients
Tobias Kowatscha,b,c,Chen-Hsuan (Iris) Shiha,Yanick X. Lukica,Olivia C. Kellera,
Katrin Heldtd,Dominique Durrere,Aikaterini Stasinakid,g,Dirk Büchterd,Björn Brogled,
Nathalie Farpour-Lambertfand Dagmar l’Allemand-Janderd,g
aCentre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
bCentre for Digital Health Interventions, Institute of Technology Management, University of St.Gallen, St.Gallen, Switzerland
cSaw Swee Hock School of Public Health, National University of Singapore, Singapore
dAdolescent Medicine, Children’s Hospital of Eastern Switzerland, St.Gallen, Switzerland
eChild and Youth School Health Service, Department of Education and Youth, Vevey, Switzerland
f
Service of Endocrinology, Diabetology, Nutrition and Therapeutic Patient Education, Department of Medicine, University Hospitals of Geneva,
Geneva, Switzerland
gPediatric Endocrinology, Children’s Hospital of Eastern Switzerland, St.Gallen, Switzerland
Abstract
Eective interventions for the prevention and treatment of child and adolescent obesity play an important role in reducing
the global health and economic burden of non-communicable diseases. Although multi-component interventions targeting
various health behaviors are deemed promising, evidence for their eectiveness is still limited. Self-regulation seems to be a
relevant working mechanism in this regard. Therefore, we propose a playful, smartphone-based self-regulation training that
also utilizes the health benets of a slow-paced breathing exercise. The mobile app uses the microphone of the smartphone to
detect breathing sounds (e.g. inhalation, exhalation) and translates these sounds into a visual biofeedback on the smartphone
screen. The design and evaluation of a very rst prototype is described in this interdisciplinary work of obesity experts,
clinical psychologists, young patients, and computer scientists. The apps’ breathing detection module uses a random forest
tree for quasi real-time classication of the incoming audio samples and biofeedback generation. A study with 11 children and
adolescents with obesity was conducted to assess the prototype. Results indicate overall positive evaluations and suggestions
for improvement. Implications and limitations are discussed, and an outlook on future work is provided.
Keywords
human-computer interaction, self-regulation, digital health intervention, biofeedback, breathing training, breathing detection
1. Introduction
Non-communicable diseases (NCDs), such as cardiovas-
cular diseases or mental disorders, are the leading cause
of death worldwide, contributing to 73% of deaths [
1
].
NCDs also lead to a signicant nancial burden [
2
,
3
],
for example, up to 90% of all health care spending in the
U.S. [4].
To address this important problem, health interven-
tions must target adverse health behaviors such as mal-
Joint Proceedings of the ACM IUI 2021 Workshops, April 13–17, 2021,
College Station, USA
tkowatsch@ethz.ch (T. Kowatsch);
dagmar.lallemand@kispisg.ch (D. l’Allemand-Jander)
https://www.c4dhi.org/ (T. Kowatsch)
0000-0001-5939-4145 (T. Kowatsch); 0000-0002-2576-6569
(Y. X. Lukic); 0000-0001-8761-8214 (O. C. Keller);
0000-0001-6478-7269 (N. Farpour-Lambert); 0000-0003-3144-3907
(D. l’Allemand-Jander)
© 2021 Copyright © 2021 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
nutrition, physical inactivity and resulting metabolic risk
factors, for example, obesity [
5
]. The earlier in life ef-
fective interventions are delivered, the lower the future
nancial burden of NCDs and the more likely the uptake
of health-promoting behaviors is due to heightened neu-
roplasticity and cognitive exibility in children and ado-
lescents [
6
,
7
]. These eorts are especially important as
child and adolescent obesity has increased substantially
worldwide [
8
], while recent systematic reviews found
only low to moderate evidence for eective interventions
[9,10].
Although evidence suggests multi-component inter-
ventions that target, for example, physical activity and
diet, underlying mechanisms for why and for whom they
work are still under investigation [
10
]. Self-regulation
has been proposed as an important mechanism in child
health [
11
] as it refers to the ”cognitive and behavioral
processes through which an individual maintains levels
of emotional, motivational, and cognitive arousal that

are conducive to positive adjustment and adaptation, as
reected in positive social relationships, productivity,
achievement, and a positive sense of self.” [
12
, p. 900] For
instance, obese children, require self-regulation skills to
resist the urge to eat unhealthy food. In this context, it
was demonstrated that children who have experienced
loss of control eating report a higher use of maladaptive
strategies for the regulation of emotions than children
without a history of loss of control eating [
13
]. Similarly,
another study identies emotional regulation as the mod-
erator for the relationship between perceived stress and
emotional eating [14].
Furthermore, a systematic review indicates that self-
regulation skills are among the best predictors of out-
comes in obesity interventions in adults [
15
]. Another
study with a representative sample of U.S. children not
only found a link between self-regulation and the risk of
obesity, but further identied that this link is stronger
between boys and girls [
16
]. Moreover, a recent system-
atic review found that interventions with self-regulation
interventions can be eective in children and adoles-
cents, with possible health benets [
17
]. All in all, self-
regulation skills represent a relevant target in multi-
component interventions for the prevention and treat-
ment of child and adolescent obesity.
To this end, we propose a playful self-regulation train-
ing for children and adolescents. The training is delivered
via a mobile app and focuses on a breathing exercise. The
app uses the microphone of a smartphone to detect in-
halation, exhalation, silence and noisy sounds to then
visually guide the user to perform a slow-paced breath-
ing training. With the help of the visual biofeedback,
breathing can be adjusted with the overall goal to im-
prove self-regulation skills. A conceptual overview of
the training is depicted in Fig. 1.
Aer a brief overview of related work in the next sec-
tion, we describe the design of a very rst prototype and
the evaluation procedure targeting obese children and
adolescents. We then present and discuss the results and
conclude with a summary and outlook on future work.
2. Related Work
Slow-paced breathing was chosen in this investigation
because it is not only a common self-regulation tool [e.g.,
18
,
19
,
20
,
21
,
22
] but also shows positive ”side” eects on
cardiac functioning and mental well-being [23,24,25].
The work of Carlier et al. [
26
] is similar to our training
as they implemented a mobile game that uses the micro-
phone of a smartphone to detect an ”ommm”-sound to
then visually guide children through a breathing exercise.
They tested their prototype with three children suer-
ing from autism spectrum disorder. However, results
indicated no eects on stress reduction or technology
Goal
Improving
self-regulation
skills
User
Smartphone
Playful
Biofeedback
Visualization
Breathing
Detection
(inhalation, exhalation,
silence, and noise)
Microphone
adaptation of breathing based
on biofeedback visualization
Slow-paced breathing
Figure 1: Concept of the self-regulation training
acceptance.
Empirical results of another related work by Shih et
al. [
27
] were more promising. They implemented a
similar self-regulation training and found positive ef-
fects on physiological outcomes and technology accep-
tance. However, the authors tested their prototype with
19 healthy university students and thus, these ndings
may not translate to children and adolescents. Another
limitation of this study is that the authors employed a
breathing detection model based on an attention-based
long short-term memory model in conjunction with a
preceding convolutional neural network which may not
run on older smartphones with limited computational
power.
Another study by Hunter et al. [
28
] investigated whether
a slow-paced breathing training with a mobile app that
features heart rate variability biofeedback aects the re-
covery from an articial stressor. They found that the
app had a signicant eect on salivary alpha amylase re-
covery while not showing a signicant eect on cortisol
recovery or self-reported stress recovery. Technically, the
app does not detect breathing but the heartbeat from the
smartphone’s rear camera in conjunction with the ash-
light. Thus, the app can present the breathing exercise’s
impact on the user’s heart rate variability. However, the
heart rate variability is not consciously controlled and its
measurement is time-delayed. Consequently, the breath-
ing training does not allow responsive user interaction.
3. Methods
The design and evaluation of the smartphone-based self-
regulation training was collaboratively carried out by
an interdisciplinary team of computer scientists, obese
children and adolescents as well as several obesity ex-
perts including physicians, psychotherapists, and diet

and sport experts from a children’s hospital. The project
described in this work was also approved by the local
ethics board. The specics of the design and evaluation
phases are outlined in the following sections.
3.1. Design of the Mobile App
3.1.1. User Interface
A focus group discussion with 11 young patients, moder-
ated by obesity experts, was conducted as a rst step to
gather design requirements for the self-regulation train-
ing. In response to that discussion, a rst conceptual
dra of the user interface was developed. The overall
goal of the self-regulation training was to ”move” a boat
sailing on an ocean towards an isle, far far away, with
the help of slow-paced breathing. Specically, exhalation
should imitate wind that blows the boat forward while in-
halation should imitate the collection of wind energy for
the next breathing cycle. A dra of this idea is depicted
in Fig. 2which also shows a distance-to-destination indi-
cator on the bottom and speech bubbles with additional
breathing instructions.
Based on feedback from the obesity experts and young
patients, several elements were dropped and revised to
further streamline the user interface. For example, the
distance indicator at the bottom and the wind energy in-
dicator on the right-hand side were removed so that users
could better focus better on the sailboat and its move-
ments toward the destination isle. Further, the speech
bubbles were replaced by a digital coach at the top of
the screen who had the role to ”guide” users through
the training. Moreover, moving clouds were introduced
to support the boat movements towards the destination
isle and to provide also a visual feedback for inhalation
sounds. In the latter ”inhalation” case, clouds gathered
together at the center of the screen while they moved
apart when inhaling. The high-level biofeedback logic
was also dened collaboratively among young patients,
obesity experts and computer scientists. It is outlined in
Algorithm 1. A prototype of the graphical user interface
was then implemented for the Android operating system.
Fig. 3shows a screenshot of that interface.
3.1.2. Instructional Video Clip
To ensure consistent and evidence-based instructions on
how to perform a slow-paced breathing training, an in-
structional video clip was produced with the help of the
involved obesity experts. This video clip would be pre-
sented to the user before she or he would perform the
self-regulation training with the app for the very rst
time. The clip explains in ca. 30s how a deep abdominal
breathing is conducted and instructs the audience to in-
hale through the nose and to exhale through the mouth
while performing circa six breath cycles per minute [
25
].
Figure 2: Dra of the App’s User Interface
To match the style of the user interface of the app, the
video clip employs a comic-like character and elements
of the mobile user interface (e.g. ocean, sailboat and
clouds). The resulting instructional video clip was shown
to four young patients who were then asked to perform
the communicated slow-paced breathing. The breathing
technique was assessed by the obesity experts according
to the guidelines communicated through the video clip
and deemed appropriate.
3.1.3. Breathing Detection
The overall goal of the breathing detection module of
the self-regulation training is to process audio signals
in quasi real time to distinguish between inhalation, ex-
halation, silence, and noise captured by a smartphone’s
microphone.
In a rst step, we aimed at assessing the technical fea-
sibility of this approach and built a database of audio
samples. Due to limited access to young patients and to
reduce the burden of them as patients, as well as due to
the feasibility character of this investigation, we decided

digital coach
messages
clouds
destination
sailing route
sailboat
You ar e do i ng g r ea t.
Keep it up!
124s
Figure 3: Annotated Screenshot of the App
to collect audio samples from four doctoral students (2
females; all between 25 and 27 years old). We asked the
doctoral students to sit comfortably in a chair in their
oce and perform a slow-paced breathing exercise for
three minutes according to the instructions of the video
clip described in Section 3.1.2. The audio was recorded
with a Samsung Galaxy S6 Edge through a customized
app that uses the Android AudioRecord API (PCM, 16bit,
44.1 kHz). The distance to the smartphone for these
recordings was about 20cm, a distance we found optimal
for the breathing exercise, too. One co-author listened to
the resulting recordings and manually cut them into sep-
arate audio les labeling them as inhalation,exhalation,
or silence. To collect additional samples for silence and
noise, the same smartphone was used to record sounds
in an oce. This resulted in audio samples of passing
cars and streetcars or the speech of oce workers, which
were manually marked as noise. Silent audio samples
were manually labeled as silence. The data collection
resulted in around 28,000 audio samples of 80ms length.
This length was chosen as it represented the minimum
buer size that could be acquired through the Android
audio engine at the time of implementation.
In a second step, we calculated for each sample the
rst 13 Mel-frequency cepstral coecients [
29
] with a
window size of 25ms and a 50% overlap. The coecients
Algorithm 1: Biofeedback Logic
Input:
detection = {inhalation, exhalation, silence,
noise}
Output: biofeedback = {clouds, sailboat, and
digital coach message}
1while destination not reached do
2switch detection do
3case inhalation do
4clouds gather together
5case exhalation do
6clouds expand
7sailboat moves towards destination
8digital coach provides positive
feedback
9case silence do
10 digital coach motivates user to inhale
11 case noise do
12 digital coach recommends to reduce
surrounding noise
13 end
14 end
were supplemented with four descriptive statistical mea-
sures. From the time-domain we used the mean, variance,
and maximum of the raw audio amplitude and from the
frequency-domain the peak frequency amplitude.
Third, and consistent with prior work that was suc-
cessful in detecting breathing patterns [
30
], a Random
Forest model was used with 100 trees, which was empiri-
cally found to result in the best prediction performance
for our data set.
Since our audio database was relatively small com-
pared to related work [e.g.,
27
] and to prevent our model
from over-tting, we applied k-fold cross-validation for
training and validation. We trained the random forest
model using the WEKA library. First, we applied a 80/20
training to test split over all four participants. Second,
we conducted 10-fold cross-validation on the training
data. Third, we tested the best model on the test set. The
performance of the model on the test set is reported in
Table 1. The results indicate that the trained model is able
to appropriately dierentiate between the four classes
for the breathing of known individuals.
Finally, the trained random forest model was inte-
grated into the mobile app using the WEKA Android
API. The feature extraction in the app was reproduced
using Java in conjunction with the audio processing li-
brary OpenIMAJ 1.3.9. The resulting predictions of the
incoming data would then trigger the animations of the
user interface outlined in Algorithm 1.

Table 1
Oline Performance of the Random-Forest Breathing Detection Algorithm
Class TPR FPR PRE F1-Score AUC-ROC
Inhalation 0.942 0.012 0.955 0.949 0.990
Exhalation 0.940 0.007 0.977 0.948 0.992
Silence 0.963 0.023 0.931 0.947 0.994
Noise 0.980 0.021 0.954 0.967 0.996
All 4 classes 0.954 0.016 0.954 0.955 0.994
Note: true positive rate (TPR), false positive rate (FPR), precision (PRE),
area under the curve receiver operating characteristic (AUC-ROC)
3.2. Evaluation Procedure
A study with obese children and adolescents was con-
ducted in the children’s hospital of the participating obe-
sity experts to assess the technical feasibility and percep-
tions of the self-regulation training. The procedure was
as follows.
First, patients were instructed to watch the educational
breathing video clip and to perform the breathing exer-
cise without the app. Obesity experts provided feedback
on their breathing to assure a correct technique.
In a second step, obesity experts handed over the An-
droid smartphone, the same model used for data collec-
tion (i.e. the Samsung S6 Edge), with the self-regulation
training app to the patients. The experts then explained
the purpose of the app and its visual feedback logic.
Finally, obesity experts asked the patients to perform
the app’s slow-paced breathing exercise. The patients’
goal was to ”sail” the sailboat to the destination. Dur-
ing the exercise and for safety purposes, obesity experts
observed the patients and intervened in case of any ad-
verse breathing activity. Moreover, they noted down
whether the goal was achieved and the sailboat reached
the destination, and how long it took the patients to get
there.
Aerwards, the patients received a questionnaire that
allowed them to assess the app. Constructs of inter-
est were adopted from technology acceptance research
[
31
,
32
] and included perceived ease of use, perceived en-
joyment, expected usefulness at home, intention to use
and perceived relaxation aer use. Consistent with prior
work [
33
,
34
], a single item per construct was used to
reduce the burden of the young patients. All items were
anchored on 7-point Likert scales ranging from strongly
disagree (-3) to strongly agree (3). All constructs and
item wordings are listed in Table 2. Finally, patients were
asked to write down any suggestions they may have to
improve the app.
4. Results
Overall, 8 female and 3 male young 9-16 year-old (M
= 12.6, SD = 2.4) children and adolescents with obesity
participated in the study. All patients were able to reach
the goal set by the training app in 40 to 120 seconds.
However, obesity experts observed that due to the playful
character of the app, three subjects started to perform
an adverse breathing pattern (e.g. hyperventilation or
extensive and long exhalation), motivated by the goal to
bring the sailboat as quickly as possible to its destination,
despite being instructed otherwise.
The descriptive statistics of the patients’ self-reported
perceptions of the participating patients are listed in Ta-
ble 2and corresponding boxplots with raw responses are
shown in Fig. 4. The high average mean values for all
constructs indicate that the patients found the training
app easy to use and conducive to relaxation at home. Ad-
ditionally, the patients reported enjoying its actual usage
and indicated that they could even imagine using the
app-based training every day. Finally, patients shared
that they were able to relax using the app. One-sample
sign tests conrmed these results as the self-reported
scores all lie signicantly above the neutral scale value
of zero.
The qualitative feedback indicated that the digital coach
moderating the self-regulation exercise should be cus-
tomizable, for example, regarding their outt. Moreover,
the training session was perceived as too short and thus,
it was suggested to extend the journey with the sailboat.
It was also suggested to add further elements to the ocean
scene, for example, additional milestones such as smaller
isles or surface marker buoys as sub-ordinate targets that
would trigger points when passing by with the sailboat.
Interestingly, there were some enquiries into whether
the training app was available on Apple’s iOS platform,
which indicated further interest in the training app.
Overall, the qualitative feedback conrmed the posi-
tive quantitative results presented above.

Table 2
Perceptions of 11 Young Children and Adolescents with Obesity
Construct Scale item wording Mean SD 95% CI p-value
Perceived ease of use I found it easy to blow the sailboat to the next island. 2.64 0.67 [2.21 3.00] < 0.001***
Perceived enjoyment I enjoyed the breathing exercise. 2.73 0.65 [3.00 3.00] < 0.001***
Expected usefulness at home I could imagine the exercise helping me relax at home. 1.55 1.04 [1.00 3.00] 0.002**
Intention to use I can imagine doing this exercise every day. 2.27 1.27 [2.00 3.00] 0.006**
Perceived relaxation With this exercise I could relax well just now. 1.45 1.29 [0.00 3.00] 0.008**
Note: confidence interval (CI); p < .001 = *** and p < .01 = ** for one-sample sign tests with 0 as test value and alternative
hypothesis being greater as 0; 7-point Likert scales were anchored from strongly disagree (-3) to strongly agree (3)
−3
strongly
disagree
−2
−1
neither 0
1
2
strongly
agree
3
Perceived
ease of use Perceived
enjoyment Expected
usefulness
at home
Intention
to use
everyday
Perceived
relaxation
Figure 4:
Boxplots and Answers of Self-reported Perceptions
of 11 Young Children and Adolescents with Obesity
5. Discussion
The current work presented a playful, smartphone-based
self-regulation training that was collaboratively devel-
oped by an interdisciplinary team of obesity experts, clin-
ical psychologists, children and adolescents with obesity,
as well as computer scientists. The evaluation of the train-
ing with 11 young obesity patients showed the technical
feasibility, as all patients were able to bring the sailboat
to its destination. Moreover, self-reports of the partici-
pating patients resulted in overall positive technology
assessments and various suggestions for improvement
were provided.
However, the evaluation also resulted in relevant in-
sights regarding potential side eects. First, the playful
biofeedback visualization motivated patients to adopt
a breathing technique that could lead to dizziness due
to hyperventilation or prolonged periods of exhalation.
A biofeedback visualization that oers time-restricted
inhalation and exhalation windows may overcome this
problem. That is, the sailboat could only be moved for-
ward during a pre-dened time window that promotes
a ”healthy” slow-paced breathing [
27
]. Another poten-
tial side eect promoted by the playful nature of the
self-regulation training might be smartphone addiction
[
35
,
36
]. Limiting the number of exercises per day could
be a solution in this regard. Finally, adding additional
playful elements as suggested by the young patients
raises the question to which degree the experiential eect
of the app can potentially cancel out the development of
self-regulation skills and other health benets of slow-
paced breathing such as calming down or strengthening
the cardiac system. Related work provides rst evidence
that both experiential and instrumental eects can coex-
ist [27,37].
This work has also several limitations. First, the of-
ine performance of the detection model is based on
a small sample of only four doctoral students and not
on breathing data from the target population. Both as-
pects, the small sample size resulting in low variance of
breathing sounds and the mismatch of model develop-
ment with population A and assessment by population
B, limit the generalizability of the ndings. Second, the
training and test sets were collected in the same context,
i.e. the same recording environment and smartphone
model, and contain breathing sounds from the same in-
dividuals. Consequently, the detection performance for
unknown individuals, other devices, or dierent envi-
ronments remains unknown. Third, the data collection
with respect to noisy environments was limited to only
one specic environment (the oce). Fourth, only one
specic biofeedback theme was evaluated, i.e. the ”ocean-
and-sailboat” theme, and thus, it is open to which degree
visual elements may have an impact on the eects of
the self-regulation training. Third, the cross-sectional
study setting in the children’s hospital does not allow to
draw any conclusions on long-term engagement with the
app. Finally, the evaluation procedure did not contain
any validated instrument to assess self-regulation and
thus, no conclusions can be drawn in this regard, too.
6. Summary and Future Work
We highlighted the relevance of self-regulation mech-
anisms in interventions targeting the prevention and
treatment of obesity in children and adolescents. We

proposed, implemented and evaluated a breathing-based
self-regulation training with young patients aected by
obesity.
However, the current work also points towards oppor-
tunities for upcoming research. First and foremost, future
work may focus on environment-agnostic breathing de-
tection that generalizes among individuals, smartphones,
headsets, and various ”distracting” soundscapes. Second,
micro-randomized trials may be conducted to assess an
optimal balance of experiential vs instrumental interface
designs. Third, guided-biofeedback interfaces may limit
cheating in breathing, which, in turn, could reduce any
adverse breathing patterns. And nally, the impact of
self-regulation training on self-regulation skills and rele-
vant lifestyle behavior should be assessed in longitudinal
eld studies that target the prevention and treatment of
child and adolescents obesity.
Acknowledgments
We would like to thank the CSS Insurance and the Swiss
National Science Foundation for their support through
grants 159289 and 162724.
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