=Paper=
{{Paper
|id=Vol-2903/IUI21WS-HEALTHI-7
|storemode=property
|title=A Playful Smartphone-based Self-regulation Training for the Prevention and Treatment of Child and Adolescent Obesity: Technical Feasibility and Perceptions of Young Patients
|pdfUrl=https://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-7.pdf
|volume=Vol-2903
|authors=Tobias Kowatsch,Chen-Hsuan (Iris) Shih,Yanick X. Lukic,Olivia C. Keller,Katrin Heldt,Dominique Durrer,Aikaterini Stasinaki,Dirk Büchter,Björn Brogle,Nathalie Farpour-Lambert,Dagmar l’Allemand-Jander
|dblpUrl=https://dblp.org/rec/conf/iui/KowatschSLKHDSB21
}}
==A Playful Smartphone-based Self-regulation Training for the Prevention and Treatment of Child and Adolescent Obesity: Technical Feasibility and Perceptions of Young Patients==
https://ceur-ws.org/Vol-2903/IUI21WS-HEALTHI-7.pdf
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-Lambertf and Dagmar l’Allemand-Janderd,g
a
Centre for Digital Health Interventions, Department of Management, Technology and Economics, ETH Zurich, Zurich, Switzerland
b
Centre for Digital Health Interventions, Institute of Technology Management, University of St.Gallen, St.Gallen, Switzerland
c
Saw Swee Hock School of Public Health, National University of Singapore, Singapore
d
Adolescent Medicine, Children’s Hospital of Eastern Switzerland, St.Gallen, Switzerland
e
Child 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
g
Pediatric Endocrinology, Children’s Hospital of Eastern Switzerland, St.Gallen, Switzerland
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 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 nutrition, physical inactivity and resulting metabolic risk
factors, for example, obesity [5]. The earlier in life ef-
Non-communicable diseases (NCDs), such as cardiovas- fective interventions are delivered, the lower the future
cular diseases or mental disorders, are the leading cause financial burden of NCDs and the more likely the uptake
of death worldwide, contributing to 73% of deaths [1]. of health-promoting behaviors is due to heightened neu-
NCDs also lead to a significant financial burden [2, 3], roplasticity and cognitive flexibility in children and ado-
for example, up to 90% of all health care spending in the lescents [6, 7]. These efforts are especially important as
U.S. [4]. child and adolescent obesity has increased substantially
To address this important problem, health interven- worldwide [8], while recent systematic reviews found
tions must target adverse health behaviors such as mal- only low to moderate evidence for effective interventions
[9, 10].
Joint Proceedings of the ACM IUI 2021 Workshops, April 13–17, 2021,
College Station, USA Although evidence suggests multi-component inter-
Envelope-Open tkowatsch@ethz.ch (T. Kowatsch); ventions that target, for example, physical activity and
dagmar.lallemand@kispisg.ch (D. l’Allemand-Jander) diet, underlying mechanisms for why and for whom they
GLOBE https://www.c4dhi.org/ (T. Kowatsch) work are still under investigation [10]. Self-regulation
Orcid 0000-0001-5939-4145 (T. Kowatsch); 0000-0002-2576-6569 has been proposed as an important mechanism in child
(Y. X. Lukic); 0000-0001-8761-8214 (O. C. Keller);
0000-0001-6478-7269 (N. Farpour-Lambert); 0000-0003-3144-3907 health [11] as it refers to the ”cognitive and behavioral
(D. l’Allemand-Jander) processes through which an individual maintains levels
© 2021 Copyright © 2021 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0). of emotional, motivational, and cognitive arousal that
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
are conducive to positive adjustment and adaptation, as
Smartphone
reflected in positive social relationships, productivity,
adaptation of breathing based
achievement, and a positive sense of self.” [12, p. 900] For Playful
on biofeedback visualization
instance, obese children, require self-regulation skills to Biofeedback
resist the urge to eat unhealthy food. In this context, it Visualization
was demonstrated that children who have experienced
loss of control eating report a higher use of maladaptive Goal
Improving
strategies for the regulation of emotions than children User self-regulation
Breathing
without a history of loss of control eating [13]. Similarly, skills Detection
(inhalation, exhalation,
another study identifies emotional regulation as the mod- silence, and noise)
erator for the relationship between perceived stress and
emotional eating [14].
Furthermore, a systematic review indicates that self- Microphone
Slow-paced breathing
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 Figure 1: Concept of the self-regulation training
only found a link between self-regulation and the risk of
obesity, but further identified that this link is stronger
between boys and girls [16]. Moreover, a recent system-
acceptance.
atic review found that interventions with self-regulation
Empirical results of another related work by Shih et
interventions can be effective in children and adoles-
al. [27] were more promising. They implemented a
cents, with possible health benefits [17]. All in all, self-
similar self-regulation training and found positive ef-
regulation skills represent a relevant target in multi-
fects on physiological outcomes and technology accep-
component interventions for the prevention and treat-
tance. However, the authors tested their prototype with
ment of child and adolescent obesity.
19 healthy university students and thus, these findings
To this end, we propose a playful self-regulation train-
may not translate to children and adolescents. Another
ing for children and adolescents. The training is delivered
limitation of this study is that the authors employed a
via a mobile app and focuses on a breathing exercise. The
breathing detection model based on an attention-based
app uses the microphone of a smartphone to detect in-
long short-term memory model in conjunction with a
halation, exhalation, silence and noisy sounds to then
preceding convolutional neural network which may not
visually guide the user to perform a slow-paced breath-
run on older smartphones with limited computational
ing training. With the help of the visual biofeedback,
power.
breathing can be adjusted with the overall goal to im-
Another study by Hunter et al. [28] investigated whether
prove self-regulation skills. A conceptual overview of
a slow-paced breathing training with a mobile app that
the training is depicted in Fig. 1.
features heart rate variability biofeedback affects the re-
After a brief overview of related work in the next sec-
covery from an artificial stressor. They found that the
tion, we describe the design of a very first prototype and
app had a significant effect on salivary alpha amylase re-
the evaluation procedure targeting obese children and
covery while not showing a significant effect on cortisol
adolescents. We then present and discuss the results and
recovery or self-reported stress recovery. Technically, the
conclude with a summary and outlook on future work.
app does not detect breathing but the heartbeat from the
smartphone’s rear camera in conjunction with the flash-
2. Related Work light. Thus, the app can present the breathing exercise’s
impact on the user’s heart rate variability. However, the
Slow-paced breathing was chosen in this investigation heart rate variability is not consciously controlled and its
because it is not only a common self-regulation tool [e.g., measurement is time-delayed. Consequently, the breath-
18, 19, 20, 21, 22] but also shows positive ”side” effects on ing training does not allow responsive user interaction.
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- 3. Methods
phone of a smartphone to detect an ”ommm”-sound to
The design and evaluation of the smartphone-based self-
then visually guide children through a breathing exercise.
regulation training was collaboratively carried out by
They tested their prototype with three children suffer-
an interdisciplinary team of computer scientists, obese
ing from autism spectrum disorder. However, results
children and adolescents as well as several obesity ex-
indicated no effects on stress reduction or technology
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 specifics 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 first step to
gather design requirements for the self-regulation train-
ing. In response to that discussion, a first conceptual
draft 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. Specifically, 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 draft of this idea is depicted
in Fig. 2 which 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 Figure 2: Draft of the App’s User Interface
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 To match the style of the user interface of the app, the
sounds. In the latter ”inhalation” case, clouds gathered video clip employs a comic-like character and elements
together at the center of the screen while they moved of the mobile user interface (e.g. ocean, sailboat and
apart when inhaling. The high-level biofeedback logic clouds). The resulting instructional video clip was shown
was also defined collaboratively among young patients, to four young patients who were then asked to perform
obesity experts and computer scientists. It is outlined in the communicated slow-paced breathing. The breathing
Algorithm 1. A prototype of the graphical user interface technique was assessed by the obesity experts according
was then implemented for the Android operating system. to the guidelines communicated through the video clip
Fig. 3 shows a screenshot of that interface. and deemed appropriate.
3.1.2. Instructional Video Clip 3.1.3. Breathing Detection
To ensure consistent and evidence-based instructions on The overall goal of the breathing detection module of
how to perform a slow-paced breathing training, an in- the self-regulation training is to process audio signals
structional video clip was produced with the help of the in quasi real time to distinguish between inhalation, ex-
involved obesity experts. This video clip would be pre- halation, silence, and noise captured by a smartphone’s
sented to the user before she or he would perform the microphone.
self-regulation training with the app for the very first In a first step, we aimed at assessing the technical fea-
time. The clip explains in ca. 30s how a deep abdominal sibility of this approach and built a database of audio
breathing is conducted and instructs the audience to in- samples. Due to limited access to young patients and to
hale through the nose and to exhale through the mouth reduce the burden of them as patients, as well as due to
while performing circa six breath cycles per minute [25]. the feasibility character of this investigation, we decided
Algorithm 1: Biofeedback Logic
You are doing great. digital coach
messages Input: detection = {inhalation, exhalation, silence,
Keep it up! noise}
124s Output: biofeedback = {clouds, sailboat, and
digital coach message}
destination 1 while destination not reached do
2 switch detection do
3 case inhalation do
4 clouds gather together
5 case exhalation do
6 clouds expand
7 sailboat moves towards destination
8 digital coach provides positive
feedback
9 case silence do
clouds 10 digital coach motivates user to inhale
11 case noise do
sailboat 12 digital coach recommends to reduce
surrounding noise
13 end
14 end
sailing route
were supplemented with four descriptive statistical mea-
Figure 3: Annotated Screenshot of the App 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-
to collect audio samples from four doctoral students (2 cessful in detecting breathing patterns [30], a Random
females; all between 25 and 27 years old). We asked the Forest model was used with 100 trees, which was empiri-
doctoral students to sit comfortably in a chair in their cally found to result in the best prediction performance
office and perform a slow-paced breathing exercise for for our data set.
three minutes according to the instructions of the video Since our audio database was relatively small com-
clip described in Section 3.1.2. The audio was recorded pared to related work [e.g., 27] and to prevent our model
with a Samsung Galaxy S6 Edge through a customized from over-fitting, we applied k-fold cross-validation for
app that uses the Android AudioRecord API (PCM, 16bit, training and validation. We trained the random forest
44.1 kHz). The distance to the smartphone for these model using the WEKA library. First, we applied a 80/20
recordings was about 20cm, a distance we found optimal training to test split over all four participants. Second,
for the breathing exercise, too. One co-author listened to we conducted 10-fold cross-validation on the training
the resulting recordings and manually cut them into sep- data. Third, we tested the best model on the test set. The
arate audio files labeling them as inhalation, exhalation, performance of the model on the test set is reported in
or silence. To collect additional samples for silence and Table 1. The results indicate that the trained model is able
noise, the same smartphone was used to record sounds to appropriately differentiate between the four classes
in an office. This resulted in audio samples of passing for the breathing of known individuals.
cars and streetcars or the speech of office workers, which Finally, the trained random forest model was inte-
were manually marked as noise. Silent audio samples grated into the mobile app using the WEKA Android
were manually labeled as silence. The data collection API. The feature extraction in the app was reproduced
resulted in around 28,000 audio samples of 80ms length. using Java in conjunction with the audio processing li-
This length was chosen as it represented the minimum brary OpenIMAJ 1.3.9. The resulting predictions of the
buffer size that could be acquired through the Android incoming data would then trigger the animations of the
audio engine at the time of implementation. user interface outlined in Algorithm 1.
In a second step, we calculated for each sample the
first 13 Mel-frequency cepstral coefficients [29] with a
window size of 25ms and a 50% overlap. The coefficients
Table 1
Offline 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 4. Results
A study with obese children and adolescents was con- Overall, 8 female and 3 male young 9-16 year-old (M
ducted in the children’s hospital of the participating obe- = 12.6, SD = 2.4) children and adolescents with obesity
sity experts to assess the technical feasibility and percep- participated in the study. All patients were able to reach
tions of the self-regulation training. The procedure was the goal set by the training app in 40 to 120 seconds.
as follows. However, obesity experts observed that due to the playful
First, patients were instructed to watch the educational character of the app, three subjects started to perform
breathing video clip and to perform the breathing exer- an adverse breathing pattern (e.g. hyperventilation or
cise without the app. Obesity experts provided feedback extensive and long exhalation), motivated by the goal to
on their breathing to assure a correct technique. bring the sailboat as quickly as possible to its destination,
In a second step, obesity experts handed over the An- despite being instructed otherwise.
droid smartphone, the same model used for data collec- The descriptive statistics of the patients’ self-reported
tion (i.e. the Samsung S6 Edge), with the self-regulation perceptions of the participating patients are listed in Ta-
training app to the patients. The experts then explained ble 2 and corresponding boxplots with raw responses are
the purpose of the app and its visual feedback logic. shown in Fig. 4. The high average mean values for all
Finally, obesity experts asked the patients to perform constructs indicate that the patients found the training
the app’s slow-paced breathing exercise. The patients’ app easy to use and conducive to relaxation at home. Ad-
goal was to ”sail” the sailboat to the destination. Dur- ditionally, the patients reported enjoying its actual usage
ing the exercise and for safety purposes, obesity experts and indicated that they could even imagine using the
observed the patients and intervened in case of any ad- app-based training every day. Finally, patients shared
verse breathing activity. Moreover, they noted down that they were able to relax using the app. One-sample
whether the goal was achieved and the sailboat reached sign tests confirmed these results as the self-reported
the destination, and how long it took the patients to get scores all lie significantly above the neutral scale value
there. of zero.
Afterwards, the patients received a questionnaire that The qualitative feedback indicated that the digital coach
allowed them to assess the app. Constructs of inter- moderating the self-regulation exercise should be cus-
est were adopted from technology acceptance research tomizable, for example, regarding their outfit. Moreover,
[31, 32] and included perceived ease of use, perceived en- the training session was perceived as too short and thus,
joyment, expected usefulness at home, intention to use it was suggested to extend the journey with the sailboat.
and perceived relaxation after use. Consistent with prior It was also suggested to add further elements to the ocean
work [33, 34], a single item per construct was used to scene, for example, additional milestones such as smaller
reduce the burden of the young patients. All items were isles or surface marker buoys as sub-ordinate targets that
anchored on 7-point Likert scales ranging from strongly would trigger points when passing by with the sailboat.
disagree (-3) to strongly agree (3). All constructs and Interestingly, there were some enquiries into whether
item wordings are listed in Table 2. Finally, patients were the training app was available on Apple’s iOS platform,
asked to write down any suggestions they may have to which indicated further interest in the training app.
improve the app. Overall, the qualitative feedback confirmed 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)
strongly
agree
3
self-regulation training might be smartphone addiction
[35, 36]. Limiting the number of exercises per day could
2
be a solution in this regard. Finally, adding additional
1 playful elements as suggested by the young patients
raises the question to which degree the experiential effect
neither 0
of the app can potentially cancel out the development of
−1
self-regulation skills and other health benefits of slow-
paced breathing such as calming down or strengthening
−2 the cardiac system. Related work provides first evidence
−3 that both experiential and instrumental effects can coex-
strongly
disagree ist [27, 37].
Perceived Perceived Expected Intention Perceived
ease of use enjoyment usefulness to use relaxation This work has also several limitations. First, the of-
at home everyday
fline performance of the detection model is based on
a small sample of only four doctoral students and not
Figure 4: Boxplots and Answers of Self-reported Perceptions on breathing data from the target population. Both as-
of 11 Young Children and Adolescents with Obesity 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
5. Discussion B, limit the generalizability of the findings. Second, the
training and test sets were collected in the same context,
The current work presented a playful, smartphone-based i.e. the same recording environment and smartphone
self-regulation training that was collaboratively devel- model, and contain breathing sounds from the same in-
oped by an interdisciplinary team of obesity experts, clin- dividuals. Consequently, the detection performance for
ical psychologists, children and adolescents with obesity, unknown individuals, other devices, or different envi-
as well as computer scientists. The evaluation of the train- ronments remains unknown. Third, the data collection
ing with 11 young obesity patients showed the technical with respect to noisy environments was limited to only
feasibility, as all patients were able to bring the sailboat one specific environment (the office). Fourth, only one
to its destination. Moreover, self-reports of the partici- specific biofeedback theme was evaluated, i.e. the ”ocean-
pating patients resulted in overall positive technology and-sailboat” theme, and thus, it is open to which degree
assessments and various suggestions for improvement visual elements may have an impact on the effects of
were provided. the self-regulation training. Third, the cross-sectional
However, the evaluation also resulted in relevant in- study setting in the children’s hospital does not allow to
sights regarding potential side effects. First, the playful draw any conclusions on long-term engagement with the
biofeedback visualization motivated patients to adopt app. Finally, the evaluation procedure did not contain
a breathing technique that could lead to dizziness due any validated instrument to assess self-regulation and
to hyperventilation or prolonged periods of exhalation. thus, no conclusions can be drawn in this regard, too.
A biofeedback visualization that offers time-restricted
inhalation and exhalation windows may overcome this
problem. That is, the sailboat could only be moved for- 6. Summary and Future Work
ward during a pre-defined time window that promotes
a ”healthy” slow-paced breathing [27]. Another poten- We highlighted the relevance of self-regulation mech-
tial side effect promoted by the playful nature of the anisms in interventions targeting the prevention and
treatment of obesity in children and adolescents. We
proposed, implemented and evaluated a breathing-based http://apps.who.int/iris/bitstream/handle/10665/
self-regulation training with young patients affected by 94384/9789241506236_eng.pdf.
obesity. [6] F. Y. Ismail, A. Fatemi, M. V. Johnston, Cerebral plas-
However, the current work also points towards oppor- ticity: Windows of opportunity in the developing
tunities for upcoming research. First and foremost, future brain, European Journal of Paediatric Neurology
work may focus on environment-agnostic breathing de- 21 (2017) 23–48. doi:1 0 . 1 0 1 6 / j . e j p n . 2 0 1 6 . 0 7 . 0 0 7 .
tection that generalizes among individuals, smartphones, [7] F. Buttelmann, J. Karbach, Development and plas-
headsets, and various ”distracting” soundscapes. Second, ticity of cognitive flexibility in early and middle
micro-randomized trials may be conducted to assess an childhood, Frontiers in Psychology 8 (2017). doi:1 0 .
optimal balance of experiential vs instrumental interface 3389/fpsyg.2017.01040.
designs. Third, guided-biofeedback interfaces may limit [8] T. Lobstein, R. Jackson-Leach, M. L. Moodie, K. D.
cheating in breathing, which, in turn, could reduce any Hall, S. L. Gortmaker, B. A. Swinburn, W. P. T. James,
adverse breathing patterns. And finally, the impact of Y. Wang, K. McPherson, Child and adolescent obe-
self-regulation training on self-regulation skills and rele- sity: part of a bigger picture, Lancet 385 (2015)
vant lifestyle behavior should be assessed in longitudinal 2510–20. doi:1 0 . 1 0 1 6 / s 0 1 4 0 - 6 7 3 6 ( 1 4 ) 6 1 7 4 6 - 3 .
field studies that target the prevention and treatment of [9] T. Brown, T. H. Moore, L. Hooper, Y. Gao, A. Za-
child and adolescents obesity. yegh, S. Ijaz, M. Elwenspoek, S. C. Foxen, L. Magee,
C. O’Malley, E. Waters, C. D. Summerbell, Interven-
tions for preventing obesity in children, Cochrane
Acknowledgments Database Syst Rev 7 (2019) Cd001871. doi:1 0 . 1 0 0 2 /
14651858.CD001871.pub4.
We would like to thank the CSS Insurance and the Swiss
[10] L. J. Ells, K. Rees, T. Brown, E. Mead, L. Al-Khudairy,
National Science Foundation for their support through
L. Azevedo, G. J. McGeechan, L. Baur, E. Love-
grants 159289 and 162724.
man, H. Clements, P. Rayco-Solon, N. Farpour-
Lambert, A. Demaio, Interventions for treating
References children and adolescents with overweight and obe-
sity: an overview of cochrane reviews, Inter-
[1] G. A. Roth, D. Abate, K. H. Abate, S. M. Abay, national Journal of Obesity 42 (2018) 1823–1833.
C. Abbafati, N. Abbasi, H. Abbastabar, F. Abd- doi:1 0 . 1 0 3 8 / s 4 1 3 6 6 - 0 1 8 - 0 2 3 0 - y .
Allah, J. Abdela, A. Abdelalim, I. Abdollahpour, [11] A. L. Miller, A. N. Gearhardt, E. M. Fredericks,
R. S. Abdulkader, H. T. Abebe, M. Abebe, Z. Abebe, B. Katz, L. F. Shapiro, K. Holden, N. Kaciroti, R. Gon-
A. N. Abejie, S. F. Abera, O. Z. Abil, H. N. Abraha, zalez, C. Hunter, J. C. Lumeng, Targeting self-
A. R. Abrham, et al., Global, regional, and na- regulation to promote health behaviors in children,
tional age-sex-specific mortality for 282 causes of Behaviour Research and Therapy 101 (2018) 71–81.
death in 195 countries and territories, 1980-2017: a doi:1 0 . 1 0 1 6 / j . b r a t . 2 0 1 7 . 0 9 . 0 0 8 .
systematic analysis for the global burden of dis- [12] C. Blair, A. Diamond, Biological processes in pre-
ease study 2017, Lancet 392 (2018) 1736–1788. vention and intervention: The promotion of self-
doi:1 0 . 1 0 1 6 / s 0 1 4 0 - 6 7 3 6 ( 1 8 ) 3 2 2 0 3 - 7 . regulation as a means of preventing school fail-
[2] D. Newman, M. Tong, E. Levine, S. Kishore, Preva- ure, Development and Psychopathology 20 (2008)
lence of multiple chronic conditions by u.s. state 899–911. doi:1 0 . 1 0 1 7 / S 0 9 5 4 5 7 9 4 0 8 0 0 0 4 3 6 .
and territory, 2017, PLOS ONE 15 (2020) e0232346. [13] J. Czaja, W. Rief, A. Hilbert, Emotion regulation
doi:1 0 . 1 3 7 1 / j o u r n a l . p o n e . 0 2 3 2 3 4 6 . and binge eating in children, Int J Eat Disord 42
[3] A. Murphy, B. Palafox, M. Walli-Attaei, T. Powell- (2009) 356–62. doi:1 0 . 1 0 0 2 / e a t . 2 0 6 3 0 .
Jackson, etal., The household economic burden [14] E. Aparicio, J. Canals, V. Arija, S. de Henauw,
of non-communicable diseases in 18 countries, N. Michels, The role of emotion regulation in child-
BMJ Global Health 5 (2020) e002040. doi:1 0 . 1 1 3 6 / hood obesity: implications for prevention and treat-
bmjgh- 2019- 002040. ment, Nutrition Research Reviews 29 (2016) 17–29.
[4] M. B. Christine Buttorff, Teague Ruder, Multi- doi:1 0 . 1 0 1 7 / S 0 9 5 4 4 2 2 4 1 5 0 0 0 1 5 3 .
ple chronic conditions in the united states, 2017. [15] P. J. Teixeira, E. V. Carraça, M. M. Marques, H. Rut-
URL: https://www.rand.org/pubs/tools/TL221.html. ter, J.-M. Oppert, I. de Bourdeaudhuij, J. Lakerveld,
doi:1 0 . 7 2 4 9 / T L 2 2 1 . J. Brug, Successful behavior change in obesity
[5] WHO, Global action plan for the prevention and interventions in adults: a systematic review of
control of noncommunicable diseases 2013–2020, self-regulation mediators, BMC Med 13 (2015) 84.
Report, World Health Organization, 2020. URL: doi:1 0 . 1 1 8 6 / s 1 2 9 1 6 - 0 1 5 - 0 3 2 3 - 6 .
[16] S. E. Anderson, R. C. Whitaker, Association of
self-regulation with obesity in boys vs girls in a PervasiveHealth’19, Association for Computing
us national sample, JAMA Pediatrics 172 (2018) Machinery, New York, NY, USA, 2019, p. 452–461.
842–850. doi:1 0 . 1 0 0 1 / j a m a p e d i a t r i c s . 2 0 1 8 . 1 4 1 3 . doi:1 0 . 1 1 4 5 / 3 3 2 9 1 8 9 . 3 3 2 9 2 3 7 .
[17] A. Pandey, D. Hale, S. Das, A.-L. Goddings, S.-J. [27] C.-H. Shih, N. Tomita, Y. X. Lukic, A. H. Reguera,
Blakemore, R. M. Viner, Effectiveness of univer- E. Fleisch, T. Kowatsch, Breeze: Smartphone-based
sal self-regulation–based interventions in children acoustic real-time detection of breathing phases for
and adolescents: A systematic review and meta- a gamified biofeedback breathing training, Proc.
analysis, JAMA Pediatrics 172 (2018) 566–575. ACM Interact. Mob. Wearable Ubiquitous Technol.
doi:1 0 . 1 0 0 1 / j a m a p e d i a t r i c s . 2 0 1 8 . 0 2 3 2 . 3 (2019) Article 152. doi:1 0 . 1 1 4 5 / 3 3 6 9 8 3 5 .
[18] M. Martin, M. Seppa, P. Lehtinen, T. Toro, [28] J. F. Hunter, M. S. Olah, A. L. Williams, A. C. Parks,
Breathing as a Tool for Self-Regulation and Self- S. D. Pressman, Effect of brief biofeedback via a
Reflection, Routledge, London, UK, 2016. doi:1 0 . smartphone app on stress recovery: Randomized
4324/9780429472572. experimental study, JMIR Serious Games 7 (2019)
[19] Y.-Y. Tang, Y. Ma, J. Wang, Y. Fan, S. Feng, Q. Lu, e15974. doi:1 0 . 2 1 9 6 / 1 5 9 7 4 .
Q. Yu, D. Sui, M. K. Rothbart, M. Fan, M. I. Pos- [29] S. B. Davis, P. Mermelstein, Comparison of paramet-
ner, Short-term meditation training improves at- ric representations for monosyllabic word recog-
tention and self-regulation, Proceedings of the Na- nition in continuously spoken sentences, IEEE
tional Academy of Sciences 104 (2007) 17152–17156. Transactions on Acoustics, Speech, and Signal Pro-
doi:1 0 . 1 0 7 3 / p n a s . 0 7 0 7 6 7 8 1 0 4 . cessing 28 (1980) 357–366. doi:1 0 . 1 1 0 9 / T A S S P . 1 9 8 0 .
[20] N. Harvey, Mindful little yogis: self-regulation tools 1163420.
to empower kids with special needs to breath and [30] T. Rosenwein, E. Dafna, A. Tarasiuk, Y. Zigel, De-
relax, Singing Dragon, London, UK, 2018. tection of breathing sounds during sleep using non-
[21] Z. Wang, A. Parnandi, R. Gutierrez-Osuna, Biopad: contact audio recordings, in: 36th Annual Inter-
Leveraging off-the-shelf video games for stress self- national Conference of the IEEE Engineering in
regulation, IEEE Journal of Biomedical and Health Medicine and Biology Society, 2014, pp. 1489–1492.
Informatics 22 (2018) 47–55. doi:1 0 . 1 1 0 9 / J B H I . 2 0 1 7 . doi:1 0 . 1 1 0 9 / E M B C . 2 0 1 4 . 6 9 4 3 8 8 3 .
2671788. [31] A. Kamis, M. Koufaris, T. Stern, Using an attribute-
[22] A. L. Eva, N. M. Thayer, Learning to breathe: A based decision support system for user-customized
pilot study of a mindfulness-based intervention to products online: An experimental investigation,
support marginalized youth, Journal of Evidence- MIS Quarterly 32 (2008) 159–177. doi:1 0 . 2 3 0 7 /
Based Complementary & Alternative Medicine 22 25148832.
(2017) 580–591. doi:1 0 . 1 1 7 7 / 2 1 5 6 5 8 7 2 1 7 6 9 6 9 2 8 . [32] F. D. Davis, Perceived usefulness, perceived ease
[23] M. C. Schumer, E. K. Lindsay, J. D. Creswell, Brief of use, and user acceptance of information technol-
mindfulness training for negative affectivity: A sys- ogy, MIS Quarterly 13 (1989) 319–339. doi:1 0 . 2 3 0 7 /
tematic review and meta-analysis, Journal of Con- 249008.
sulting and Clinical Psychology 86 (2018) 569–583. [33] T. Kowatsch, D. Volland, I. Shih, D. Rüegger, F. Kün-
doi:1 0 . 1 0 3 7 / c c p 0 0 0 0 3 2 4 . zler, F. Barata, A. Filler, D. Büchter, B. Brogle,
[24] A. Zaccaro, A. Piarulli, M. Laurino, E. Garbella, K. Heldt, P. Gindrat, N. Farpour-Lambert, D. l’Alle-
D. Menicucci, B. Neri, A. Gemignani, How breath- mand, Design and Evaluation of a Mobile Chat App
control can change your life: A systematic review for the Open Source Behavioral Health Intervention
on psycho-physiological correlates of slow breath- Platform MobileCoach, Springer, Berlin; Germany,
ing, Frontiers in Human Neuroscience 12 (2018). 2017, pp. 485–489. doi:1 0 . 1 0 0 7 / 9 7 8 - 3 - 3 1 9 - 5 9 1 4 4 - 5 _
doi:1 0 . 3 3 8 9 / f n h u m . 2 0 1 8 . 0 0 3 5 3 . 36.
[25] M. E. B. Russell, A. B. Scott, I. A. Boggero, C. R. [34] T. Kowatsch, W. Maass, I. P. Cvijikj, D. Büchter,
Carlson, Inclusion of a rest period in diaphragmatic B. Brogle, A. Dintheer, D. Wiegand, D. Durrer, R. Xu,
breathing increases high frequency heart rate vari- Y. Schutz, D. l. Dagmar, Design of a health informa-
ability: Implications for behavioral therapy, Psy- tion system enhancing the performance of obesity
chophysiology 54 (2017) 358–365. doi:1 0 . 1 1 1 1 / p s y p . expert and children teams, in: Proc 22nd Euro-
12791. pean Conference on Information Systems (ECIS),
[26] S. Carlier, S. Van der Paelt, F. Ongenae, Tel Aviv, Israel, 2014.
F. De Backere, F. De Turck, Using a serious [35] S. Haug, R. P. Castro, M. Kwon, A. Filler,
game to reduce stress and anxiety in children T. Kowatsch, M. P. Schaub, Smartphone use and
with autism spectrum disorder, in: Proceedings smartphone addiction among young people in
of the 13th EAI International Conference on switzerland, Journal of Behavioral Addictions 4
Pervasive Computing Technologies for Healthcare, (2015) 299–307. doi:1 0 . 1 5 5 6 / 2 0 0 6 . 4 . 2 0 1 5 . 0 3 7 .
[36] M. Kwon, D.-J. Kim, H. Cho, S. Yang, The smart-
phone addiction scale: Development and validation
of a short version for adolescents, PLOS ONE 8
(2014) e83558. doi:1 0 . 1 3 7 1 / j o u r n a l . p o n e . 0 0 8 3 5 5 8 .
[37] Y. X. Lukic, C.-H. I. Shih, A. H. Reguera, A. Cotti,
E. Fleisch, T. Kowatsch, Physiological responses
and user feedback on a gameful breathing train-
ing app: Within-subject experiment, JMIR Serious
Games 9 (2021). doi:1 0 . 2 1 9 6 / 2 2 8 0 2 .