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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 and Figures

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.
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A Playful Smartphone-based Self-regulation Training for
the Prevention and Treatment of Child and Adolescent
Obesity: Technical Feasibility and Perceptions of Young
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
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
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.
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 [
NCDs also lead to a signicant nancial burden [
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 (T. Kowatsch); (D. l’Allemand-Jander) (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).
ISSN 1613-0073
CEUR Workshop Proceedings (
nutrition, physical inactivity and resulting metabolic risk
factors, for example, obesity [
]. 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 [
]. These eorts are especially important as
child and adolescent obesity has increased substantially
worldwide [
], while recent systematic reviews found
only low to moderate evidence for eective interventions
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 [
]. Self-regulation
has been proposed as an important mechanism in child
health [
] 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.” [
, 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 [
]. 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 [
]. 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 [
]. 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 [
]. 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.,
] but also shows positive ”side” eects on
cardiac functioning and mental well-being [23,24,25].
The work of Carlier et al. [
] 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
(inhalation, exhalation,
silence, and noise)
adaptation of breathing based
on biofeedback visualization
Slow-paced breathing
Figure 1: Concept of the self-regulation training
Empirical results of another related work by Shih et
al. [
] 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
Another study by Hunter et al. [
] 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 [
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
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
sailing route
You ar e do i ng g r ea t.
Keep it up!
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 [
] with a
window size of 25ms and a 50% overlap. The coecients
Algorithm 1: Biofeedback Logic
detection = {inhalation, exhalation, silence,
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
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 [
], 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.,
] 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
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
Aerwards, the patients received a questionnaire that
allowed them to assess the app. Constructs of inter-
est were adopted from technology acceptance research
] 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 [
], 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)
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 [
]. Another poten-
tial side eect promoted by the playful nature of the
self-regulation training might be smartphone addiction
]. 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
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.
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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... Our group initially developed the smartphone-based biofeedback breathing training Breeze and further adapted it to address the objectives of this study [48][49][50]71]. Breeze uses the smartphone's microphone to continuously detect breathing phases in real-time (i.e., inhalations, exhalations, and pauses between inhalations and exhalations). ...
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Background: Depression remains a global health problem, with its prevalence rising worldwide. Digital biomarkers are increasingly investigated to initiate and tailor scalable interventions targeting depression. Due to the steady influx of new cases, focusing on treatment alone will not suffice; academics and practitioners need to focus on the prevention of depression (i.e., addressing subclinical depression). Aim: With our study, we aim to (i) develop digital biomarkers for subclinical symptoms of depression, (ii) develop digital biomarkers for severity of subclinical depression, and (iii) investigate the efficacy of a digital intervention in reducing symptoms and severity of subclinical depression. Method: Participants will interact with the digital intervention BEDDA consisting of a scripted conversational agent, the slow-paced breathing training Breeze, and actionable advice for different symptoms. The intervention comprises 30 daily interactions to be completed in less than 45 days. We will collect self-reports regarding mood, agitation, anhedonia (proximal outcomes; first objective), self-reports regarding depression severity (primary distal outcome; second and third objective), anxiety severity (secondary distal outcome; second and third objective), stress (secondary distal outcome; second and third objective), voice, and breathing. A subsample of 25% of the participants will use smartwatches to record physiological data (e.g., heart-rate, heart-rate variability), which will be used in the analyses for all three objectives. Discussion Digital voice- and breathing-based biomarkers may improve diagnosis, prevention, and care by enabling an unobtrusive and either complementary or alternative assessment to self-reports. Furthermore, our results may advance our understanding of underlying psychophysiological changes in subclinical depression. Our study also provides further evidence regarding the efficacy of standalone digital health interventions to prevent depression. Trial registration: Ethics approval was provided by the Ethics Commission of ETH Zurich (EK-2022-N-31) and the
The way we breathe fundamentally influences our psychophysiological system. Respiration is indeed not only a valid factor for relaxation and mindfulness but also for perceived workload and exertion during motion. Especially controlled slow breathing is found to be highly advantageous during physical activity, as it fosters positive effects on the psychophysiological well-being and can also be manipulated effectively to enhance the running experience. In order to persuade runners to follow certain breathing strategies (e.g. to couple breathing rate with stride rate) the runner needs to be aware of their breathing during running. The use of visual feedback to guide the user and pursue an aspired breathing pattern during running is a promising approach as it is an established method known to enhance breathing awareness and paced breathing in sedentary training settings. Since the potential of gamification for persuasive systems has been established in the PT community, enhancing breathing awareness through a gamified visualization seems to be a promising approach. This paper presents a Gamified Breathing Training Application (GBTA) along with an exploratory study (N=11) investigating the effects of the developed application with three sequential visual feedback scenarios (with and without biofeedback) during treadmill running. Our work focuses on the exploration of changes in conscious breath-control before and after using the GBTA, subjective perception of the breathing alignment process, and the perceived effectiveness of the application. Results show a significant improvement in conscious breath-control after using the GBTA. Further on qualitative user feedback strongly indicates a perceived effectiveness of the GBTA in drawing attention to the own breath during the run and thus facilitated breathing alignment. Overall, our findings suggest a high potential of using further iterations of the GBTA during the run to raise conscious breathing-control and actively engage users in the breathing change process, to facilitate the adaptation towards an aspired breathing pattern.
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Background Slow-paced breathing training (6 breaths per minute [BPM]) improves physiological and psychological well-being by inducing relaxation characterized by increased heart rate variability (HRV). However, classic breathing training has a limited target group, and retention rates are very low. Although a gameful approach may help overcome these challenges, it is crucial to enable breathing training in a scalable context (eg, smartphone only) and ensure that they remain effective. However, despite the health benefits, no validated mobile gameful breathing training featuring a biofeedback component based on breathing seems to exist. Objective This study aims to describe the design choices and their implementation in a concrete mobile gameful breathing training app. Furthermore, it aims to deliver an initial validation of the efficacy of the resulting app. Methods Previous work was used to derive informed design choices, which, in turn, were applied to build the gameful breathing training app Breeze. In a pretest (n=3), design weaknesses in Breeze were identified, and Breeze was adjusted accordingly. The app was then evaluated in a pilot study (n=16). To ascertain that the effectiveness was maintained, recordings of breathing rates and HRV-derived measures (eg, root mean square of the successive differences [RMSSDs]) were collected. We compared 3 stages: baseline, standard breathing training deployed on a smartphone, and Breeze. ResultsOverall, 5 design choices were made: use of cool colors, natural settings, tightly incorporated game elements, game mechanics reflecting physiological measures, and a light narrative and progression model. Breeze was effective, as it resulted in a slow-paced breathing rate of 6 BPM, which, in turn, resulted in significantly increased HRV measures compared with baseline (P
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Having multiple (two or more) chronic conditions (MCC) is associated with an increased risk of mortality and functional decline, health resource utilization, and healthcare expenditures. As a result, understanding the prevalence of MCC is increasingly being recognized as a public health imperative. This research describes the prevalence and distribution of adults with MCC across the United States using 2017 data from the Behavioral Risk Factors Surveillance System (BRFSS). Prevalence of MCC was calculated for each U.S. state and territory overall, by sex and by age. Additionally, the most common condition dyads (two condition combinations) and triads (three condition combinations) were assessed for each state. Prevalence of MCC ranged from 37.9% in the District of Columbia to 64.4% in West Virginia. Females had a higher prevalence than males in 47 of 53 states and territories, and MCC prevalence increased with age in every state and territory. Overall prevalence estimates were higher than estimates using data from the National Health Interview Survey (NHIS), especially in the younger population (aged 18–44), due partly to the inclusion of high cholesterol, obesity, and depression as chronic conditions. Analysis of the most prevalent dyads and triads revealed the greatest state-by-state variability in the 18-44-year-old population. Multiple states’ most prevalent dyads and triads for this population included obesity and depression. These findings build an accurate picture of the prevalence of multiple chronic conditions across the United States and will aid public health officials in creating programs targeted to their region.
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Background Non-communicable diseases (NCDs) are the leading cause of death globally. In 2014, the United Nations committed to reducing premature mortality from NCDs, including by reducing the burden of healthcare costs. Since 2014, the Prospective Urban and Rural Epidemiology (PURE) Study has been collecting health expenditure data from households with NCDs in 18 countries. Methods Using data from the PURE Study, we estimated risk of catastrophic health spending and impoverishment among households with at least one person with NCDs (cardiovascular disease, diabetes, kidney disease, cancer and respiratory diseases; n=17 435), with hypertension only (a leading risk factor for NCDs; n=11 831) or with neither (n=22 654) by country income group: high-income countries (Canada and Sweden), upper middle income countries (UMICs: Brazil, Chile, Malaysia, Poland, South Africa and Turkey), lower middle income countries (LMICs: the Philippines, Colombia, India, Iran and the Occupied Palestinian Territory) and low-income countries (LICs: Bangladesh, Pakistan, Zimbabwe and Tanzania) and China. Results The prevalence of catastrophic spending and impoverishment is highest among households with NCDs in LMICs and China. After adjusting for covariates that might drive health expenditure, the absolute risk of catastrophic spending is higher in households with NCDs compared with no NCDs in LMICs (risk difference=1.71%; 95% CI 0.75 to 2.67), UMICs (0.82%; 95% CI 0.37 to 1.27) and China (7.52%; 95% CI 5.88 to 9.16). A similar pattern is observed in UMICs and China for impoverishment. A high proportion of those with NCDs in LICs, especially women (38.7% compared with 12.6% in men), reported not taking medication due to costs. Conclusions Our findings show that financial protection from healthcare costs for people with NCDs is inadequate, particularly in LMICs and China. While the burden of NCD care may appear greatest in LMICs and China, the burden in LICs may be masked by care foregone due to costs. The high proportion of women reporting foregone care due to cost may in part explain gender inequality in treatment of NCDs.
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Slow-paced biofeedback-guided breathing training has been shown to improve cardiac functioning and psychological well-being. Current training options, however, attract only a fraction of individuals and are limited in their scalability as they require dedicated biofeedback hardware. In this work, we present Breeze, a mobile application that uses a smartphone's microphone to continuously detect breathing phases, which then trigger a gamified biofeedback-guided breathing training. Circa 2.76 million breathing sounds from 43 subjects and control sounds were collected and labeled to train and test our breathing detection algorithm. We model breathing as inhalation-pause-exhalation-pause sequences and implement a phase-detection system with an attention-based LSTM model in conjunction with a CNN-based breath extraction module. A biofeedback-guided breathing training with Breeze takes place in real-time and achieves 75.5% accuracy in breathing phases detection. Breeze was also evaluated in a pilot study with 16 new subjects, which demonstrated that the majority of subjects prefer Breeze over a validated active control condition in its usefulness, enjoyment, control, and usage intentions. Breeze is also effective for strengthening users' cardiac functioning by increasing high-frequency heart rate variability. The results of our study suggest that Breeze could potentially be utilized in clinical and self-care activities.
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Background: Smartphones are often vilified for negatively influencing well-being and contributing to stress. However, these devices may, in fact, be useful in times of stress and, in particular, aid in stress recovery. Mobile apps that deliver evidence-based techniques for stress reduction, such as heart rate variability biofeedback (HRVB) training, hold promise as convenient, accessible, and effective stress-reducing tools. Numerous mobile health apps that may potentially aid in stress recovery are available, but very few have demonstrated that they can influence health-related physiological stress parameters (eg, salivary biomarkers of stress). The ability to recover swiftly from stress and reduce physiological arousal is particularly important for long-term health, and thus, it is imperative that evidence is provided to demonstrate the effectiveness of stress-reducing mobile health apps in this context. Objective: The purpose of this research was to investigate the physiological and psychological effects of using a smartphone app for HRVB training following a stressful experience. The efficacy of the gamified Breather component of the Happify mobile health app was examined in an experimental setting. Methods: In this study, participants (N=140) underwent a laboratory stressor and were randomly assigned to recover in one of three ways: with no phone present, with a phone present, with the HRBV game. Those in the no phone condition had no access to their phone. Those in the phone present condition had their phone but did not use it. Those in the HRVB game condition used the serious game Breather on the Happify app. Stress recovery was assessed via repeated measures of salivary alpha amylase, cortisol, and self-reported acute stress (on a 1-100 scale). Results: Participants in the HRVB game condition had significantly lower levels of salivary alpha amylase during recovery than participants in the other conditions (F2,133=3.78, P=.03). There were no significant differences among the conditions during recovery for salivary cortisol levels or self-reported stress. Conclusions: These results show that engaging in a brief HRVB training session on a smartphone reduces levels of salivary alpha amylase following a stressful experience, providing preliminary evidence for the effectiveness of Breather in improving physiological stress recovery. Given the known ties between stress recovery and future well-being, this study provides a possible mechanism by which gamified biofeedback apps may lead to better health.
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Background Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries—Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised. Findings At the broadest grouping of causes of death (Level 1), non-communicable diseases (NCDs) comprised the greatest fraction of deaths, contributing to 73·4% (95% uncertainty interval [UI] 72·5–74·1) of total deaths in 2017, while communicable, maternal, neonatal, and nutritional (CMNN) causes accounted for 18·6% (17·9–19·6), and injuries 8·0% (7·7–8·2). Total numbers of deaths from NCD causes increased from 2007 to 2017 by 22·7% (21·5–23·9), representing an additional 7·61 million (7·20–8·01) deaths estimated in 2017 versus 2007. The death rate from NCDs decreased globally by 7·9% (7·0–8·8). The number of deaths for CMNN causes decreased by 22·2% (20·0–24·0) and the death rate by 31·8% (30·1–33·3). Total deaths from injuries increased by 2·3% (0·5–4·0) between 2007 and 2017, and the death rate from injuries decreased by 13·7% (12·2–15·1) to 57·9 deaths (55·9–59·2) per 100 000 in 2017. Deaths from substance use disorders also increased, rising from 284 000 deaths (268 000–289 000) globally in 2007 to 352 000 (334 000–363 000) in 2017. Between 2007 and 2017, total deaths from conflict and terrorism increased by 118·0% (88·8–148·6). A greater reduction in total deaths and death rates was observed for some CMNN causes among children younger than 5 years than for older adults, such as a 36·4% (32·2–40·6) reduction in deaths from lower respiratory infections for children younger than 5 years compared with a 33·6% (31·2–36·1) increase in adults older than 70 years. Globally, the number of deaths was greater for men than for women at most ages in 2017, except at ages older than 85 years. Trends in global YLLs reflect an epidemiological transition, with decreases in total YLLs from enteric infections, respiratory infections and tuberculosis, and maternal and neonatal disorders between 1990 and 2017; these were generally greater in magnitude at the lowest levels of the Socio-demographic Index (SDI). At the same time, there were large increases in YLLs from neoplasms and cardiovascular diseases. YLL rates decreased across the five leading Level 2 causes in all SDI quintiles. The leading causes of YLLs in 1990—neonatal disorders, lower respiratory infections, and diarrhoeal diseases—were ranked second, fourth, and fifth, in 2017. Meanwhile, estimated YLLs increased for ischaemic heart disease (ranked first in 2017) and stroke (ranked third), even though YLL rates decreased. Population growth contributed to increased total deaths across the 20 leading Level 2 causes of mortality between 2007 and 2017. Decreases in the cause-specific mortality rate reduced the effect of population growth for all but three causes: substance use disorders, neurological disorders, and skin and subcutaneous diseases. Interpretation Improvements in global health have been unevenly distributed among populations. Deaths due to injuries, substance use disorders, armed conflict and terrorism, neoplasms, and cardiovascular disease are expanding threats to global health. For causes of death such as lower respiratory and enteric infections, more rapid progress occurred for children than for the oldest adults, and there is continuing disparity in mortality rates by sex across age groups. Reductions in the death rate of some common diseases are themselves slowing or have ceased, primarily for NCDs, and the death rate for selected causes has increased in the past decade.
IMPORTANCE: Childhood and adolescence self-regulation (SR) is gaining importance as a target of intervention because of mounting evidence of its positive associations with health, social and educational outcomes. OBJECTIVE: To conduct a systematic review and meta-analysis of rigorously evaluated interventions to improve self-regulation in children and adolescents. DATA SOURCES: Keyword searches of the PsycINFO, PubMed, EMBASE, CINAHL Plus, ERIC, British Education Index, Child Development and Adolescent Studies, and CENTRAL were used to identify all studies published through July 2016. STUDY SELECTION: To be eligible for this review, studies had to report cluster randomized trials or randomized clinical trials, evaluate universal interventions designed to improve self-regulation in children and adolescents aged 0 to 19 years, include outcomes associated with self-regulation skills, and be published in a peer-reviewed journal with the full text available in English. DATA EXTRACTION AND SYNTHESIS: A total of 14 369 published records were screened, of which 147 were identified for full-text review and 49 studies reporting 50 interventions were included in the final review. Results were summarized by narrative review and meta-analysis. MAIN OUTCOMES AND MEASURES: Self-regulation outcomes in children and adolescents. RESULTS: This review identified 17 cluster randomized trials and 32 randomized clinical trials evaluating self-regulation interventions, which included a total of 23 098 participants ranging in age from 2 to 17 years (median age, 6.0 years). Consistent improvement in self-regulation was reported in 16 of 21 curriculum-based interventions (76%), 4 of the 8 mindfulness and yoga interventions (50%), 5 of 9 family-based programs (56%), 4 of 6 exercise-based programs (67%), and 4 of 6 social and personal skills interventions (67%), or a total of 33 of 50 interventions (66%). A meta-analysis evaluating associations of interventions with self-regulation task performance scores showed a positive effect of such interventions with pooled effect size of 0.42 (95% CI, 0.32-0.53). Only 24 studies reported data on distal outcomes (29 outcomes). Positive associations were reported in 11 of 13 studies (85%) on academic achievement, 4 of 5 studies on substance abuse (80%), and in all studies reporting on conduct disorders (n = 3), studies on social skills (n = 2), studies on depression (n = 2), studies on behavioral problems (n = 2), and study on school suspensions (n = 1). No effect was seen on 2 studies reporting on academic achievement, 1 study reporting on substance abuse, and 1 additional study reporting on psychological well-being. CONCLUSIONS AND RELEVANCE: A wide range of interventions were successful in improving self-regulation in children and adolescents. There was improvement in distal academic, health, and behavioral outcomes in most intervention groups compared with controls.
Background: Prevention of childhood obesity is an international public health priority given the significant impact of obesity on acute and chronic diseases, general health, development and well-being. The international evidence base for strategies to prevent obesity is very large and is accumulating rapidly. This is an update of a previous review. Objectives: To determine the effectiveness of a range of interventions that include diet or physical activity components, or both, designed to prevent obesity in children. Search methods: We searched CENTRAL, MEDLINE, Embase, PsychINFO and CINAHL in June 2015. We re-ran the search from June 2015 to January 2018 and included a search of trial registers. Selection criteria: Randomised controlled trials (RCTs) of diet or physical activity interventions, or combined diet and physical activity interventions, for preventing overweight or obesity in children (0-17 years) that reported outcomes at a minimum of 12 weeks from baseline. Data collection and analysis: Two authors independently extracted data, assessed risk-of-bias and evaluated overall certainty of the evidence using GRADE. We extracted data on adiposity outcomes, sociodemographic characteristics, adverse events, intervention process and costs. We meta-analysed data as guided by the Cochrane Handbook for Systematic Reviews of Interventions and presented separate meta-analyses by age group for child 0 to 5 years, 6 to 12 years, and 13 to 18 years for zBMI and BMI. Main results: We included 153 RCTs, mostly from the USA or Europe. Thirteen studies were based in upper-middle-income countries (UMIC: Brazil, Ecuador, Lebanon, Mexico, Thailand, Turkey, US-Mexico border), and one was based in a lower middle-income country (LMIC: Egypt). The majority (85) targeted children aged 6 to 12 years.Children aged 0-5 years: There is moderate-certainty evidence from 16 RCTs (n = 6261) that diet combined with physical activity interventions, compared with control, reduced BMI (mean difference (MD) -0.07 kg/m2, 95% confidence interval (CI) -0.14 to -0.01), and had a similar effect (11 RCTs, n = 5536) on zBMI (MD -0.11, 95% CI -0.21 to 0.01). Neither diet (moderate-certainty evidence) nor physical activity interventions alone (high-certainty evidence) compared with control reduced BMI (physical activity alone: MD -0.22 kg/m2, 95% CI -0.44 to 0.01) or zBMI (diet alone: MD -0.14, 95% CI -0.32 to 0.04; physical activity alone: MD 0.01, 95% CI -0.10 to 0.13) in children aged 0-5 years.Children aged 6 to 12 years: There is moderate-certainty evidence from 14 RCTs (n = 16,410) that physical activity interventions, compared with control, reduced BMI (MD -0.10 kg/m2, 95% CI -0.14 to -0.05). However, there is moderate-certainty evidence that they had little or no effect on zBMI (MD -0.02, 95% CI -0.06 to 0.02). There is low-certainty evidence from 20 RCTs (n = 24,043) that diet combined with physical activity interventions, compared with control, reduced zBMI (MD -0.05 kg/m2, 95% CI -0.10 to -0.01). There is high-certainty evidence that diet interventions, compared with control, had little impact on zBMI (MD -0.03, 95% CI -0.06 to 0.01) or BMI (-0.02 kg/m2, 95% CI -0.11 to 0.06).Children aged 13 to 18 years: There is very low-certainty evidence that physical activity interventions, compared with control reduced BMI (MD -1.53 kg/m2, 95% CI -2.67 to -0.39; 4 RCTs; n = 720); and low-certainty evidence for a reduction in zBMI (MD -0.2, 95% CI -0.3 to -0.1; 1 RCT; n = 100). There is low-certainty evidence from eight RCTs (n = 16,583) that diet combined with physical activity interventions, compared with control, had no effect on BMI (MD -0.02 kg/m2, 95% CI -0.10 to 0.05); or zBMI (MD 0.01, 95% CI -0.05 to 0.07; 6 RCTs; n = 16,543). Evidence from two RCTs (low-certainty evidence; n = 294) found no effect of diet interventions on BMI.Direct comparisons of interventions: Two RCTs reported data directly comparing diet with either physical activity or diet combined with physical activity interventions for children aged 6 to 12 years and reported no differences.Heterogeneity was apparent in the results from all three age groups, which could not be entirely explained by setting or duration of the interventions. Where reported, interventions did not appear to result in adverse effects (16 RCTs) or increase health inequalities (gender: 30 RCTs; socioeconomic status: 18 RCTs), although relatively few studies examined these factors.Re-running the searches in January 2018 identified 315 records with potential relevance to this review, which will be synthesised in the next update. Authors' conclusions: Interventions that include diet combined with physical activity interventions can reduce the risk of obesity (zBMI and BMI) in young children aged 0 to 5 years. There is weaker evidence from a single study that dietary interventions may be beneficial.However, interventions that focus only on physical activity do not appear to be effective in children of this age. In contrast, interventions that only focus on physical activity can reduce the risk of obesity (BMI) in children aged 6 to 12 years, and adolescents aged 13 to 18 years. In these age groups, there is no evidence that interventions that only focus on diet are effective, and some evidence that diet combined with physical activity interventions may be effective. Importantly, this updated review also suggests that interventions to prevent childhood obesity do not appear to result in adverse effects or health inequalities.The review will not be updated in its current form. To manage the growth in RCTs of child obesity prevention interventions, in future, this review will be split into three separate reviews based on child age.