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Personal MobileCoach: tailoring behavioral interventions to the needs of individual participants

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MobileCoach, an open source behavioral intervention platform, has been developed to provide health professionals with an authoring tool to design evidence-based, scalable and low-cost digital health interventions (DHI). Its potential meets the lack in resources and capacity of health care systems to provide DHI for the treatment of noncommunicable diseases. In the current work, we introduce the first personalization approach for MobileCoach with the purpose of identifying the needs of participants, tailoring the treatment and, as a consequence, enhancing the capability of MobileCoach-based DHIs. The personalization approach is then exemplified by a very first prototype of a DHI for people with asthma that is able to detect coughing by just using a smartphone's microphone. First empirical results with five healthy subjects and 80 coughs indicate its technical feasibility as the detection accuracy yielded 83.3%. Future work will focus on the integration of personalized sensing and supporting applications for MobileCoach.
Personal MobileCoach: Tailoring Be-
havioral Interventions to the Needs of
Individual Participants
MobileCoach, an open source behavioral intervention platform,
has been developed to provide health professionals with an
authoring tool to design evidence-based, scalable and
low-cost digital health interventions (DHI). Its potential
meets the lack in resources and capacity of health care
systems to provide DHI for the treatment of noncom-
municable diseases. In the current work, we introduce
the first personalization approach for MobileCoach with
the purpose of identifying the needs of participants,
tailoring the treatment and, as a consequence, enhanc-
ing the capability of MobileCoach-based DHIs. The per-
sonalization approach is then exemplified by a very first
prototype of a DHI for people with asthma that is able
to detect coughing by just using a smartphone’s micro-
phone. First empirical results with five healthy subjects
and 80 coughs indicate its technical feasibility as the
detection accuracy yielded 83.3%. Future work will
focus on the integration of personalized sensing and
supporting applications for MobileCoach.
Author Keywords
Open Source Platform; Behavioral Intervention; Public
Health; Personalization; Machine Learning;
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Filipe Barata
ETH Zürich
8092 Zürich, CH
Tobias Kowatsch
University St.Gallen & ETH
9000 St.Gallen, CH
Peter Tinschert
University St.Gallen
9000 St.Gallen, CH
Andreas Filler
University of Bamberg & ETH
96047 Bamberg, DE
ACM Keywords
J.3 Life and Medical Science; I.2 Artificial Intelligence;
K.3.2 Learning; J.4 Social and Behavioral Sciences;
F.1.1 Models of Computation
To support an individual to adopt a health-enhancing
behavior has been the motivation for the design of
effective and efficient health interventions. Based on
behavioral health models [1, 9, 13, 17, 24] and tech-
niques [1, 22], behavior change interventions have
been the basis for several applications in the health
domain and investigated thoroughly in the last dec-
ades. The existing bottleneck at resources and the ca-
pacity to provide behavioral health interventions for the
treatment of noncommunicable diseases (NCDs) pose
one of the greatest challenges for health care systems
worldwide [27]. Furthermore, NCDs represented the
leading cause of death in 2012 responsible for more
than two thirds of the global deaths [5].
With respect to these shortcomings, Internet of Person-
al Health systems, in particular digital health interven-
tions (DHIs), have the potential to play a crucial role in
reducing the burden for an individual patient as well as
for the whole health care system. Not only could they
create the possibility of improving the effectiveness and
efficiency of health interventions, but also reduce their
costs [2]. In contrast to their potential, the design and
implementation of evidence-based, scalable and low-
cost DHI is sparsely empirically supported [23].
In this context, MobileCoach, an open source platform
for DHIs, was introduced with the main objective “to
give scientists, public and behavioral health experts a
software platform that allows them to design evidence-
based, scalable and low-cost health interventions.” [8,
p. 1] With a modular design, which enables developers
of DHIs to extend and expand MobileCoach to their
specific needs, it is the rule-based engine which moni-
tors health states and triggers state transitions that lies
at its heart. Moreover, MobileCoach bears the potential
for the much-needed scalable and low-cost supply of
DHIs to address the global healthcare insufficiencies,
especially with regard to the previously mentioned
Asthma, a chronic disease involving the airways and
the lungs, ranks among the most prevalent NCDs. 30
million children and adults (less than 45 years old)
suffer from it in Europe [10] and 3,630 people die from
it each year in the USA [7]. According to clinical guide-
lines, asthma control, a term used to describe the
course of the disease, can be assessed by symptoms
such as wheezing, breathlessness, chest tightness and
coughing. Coughing, particularly the amount of coughs
per day or per night, is reported to provide an objective
assessment which correlates with the standard meas-
ure of asthma control [21].
In addition to being used as a measure of asthma con-
trol, coughing, a common symptom for many respirato-
ry diseases [15], is a well-recognized indicator for the
improvement of diagnostics. As a consequence, many
efforts have been made towards the development of
objective audio cough monitoring systems, which can
be traced all the way back to the 1950s [3]. Recent
advances have been accomplished by employing ma-
chine learning to automatically detect coughs into a
semi-automated [4] or fully automated procedure [19].
The latter is a smartphone-based solution that merely
makes use of a smartphone’s built-in microphone to
record coughing sounds and to subsequently detect and
count personal coughs.
In conclusion, a smartphone not only enables recording
and monitoring of cough sounds in near real-time but
also provides a low-cost and scalable scope for a sens-
ing application which is able to trigger health interven-
tions for people with asthma based on an objective
assessment of asthma control. In this current work we
elaborate on the development and design of the afore-
said application for the purpose of integrating it into the
MobileCoach platform as a trigger for future behavioral
health interventions. Furthermore, we want to enhance
the capability of such a sensing application by using
machine learning techniques for personalization.
Personalization is known from the web customization
process in which organizations determine the needs of
the users and provide them advertising services, shop-
ping services and filtering without the users having to
ask for it explicitly [28]. In the context of MobileCoach,
personalization may first help identifying the needs of
the participants in the intervention more precisely and
therefore develop a more sensitive trigger for DHIs,
second it may help to tailor DHIs themselves, hence
enhance their capability. With regard to the sensing
application for cough monitoring this could mean that
the system recognizes when it may lack the capability
to monitor the participant’s coughing rates precisely
and therefore start on its own behalf to adapt the sys-
tem’s model by learning from the participantindividual
The remainder of this current work is organized as
follows. We briefly outline a novel personalization ap-
proach for MobileCoach. We then describe coughing
detection by means of a smartphone, present first em-
pirical results from a technical feasibility study and
describe how it can be related to a personalized Mo-
bileCoach DHI. We conclude with a summary and an
outlook on future work.
The personal MobileCoach
In order to add personalization to the current Mo-
bileCoach, we illustrate its conceptual design first. The
MobileCoach system as it has been introduced [8] fol-
lows the sequential logic of a state machine, where the
state transitions are determined by intervention rules.
The state corresponds to an aggregation of all signifi-
cant variables, which are relevant to the intervention
progress of the participant. Whenever a state transition
is triggered by an intervention rule, significant partici-
pant variables are changed and therefore a change in
the state machine occurs.
In particular, a participant registers himself / herself to
the system by filling out an online baseline assessment
(e.g. nickname, age, mobile number, intentions and the
like), the consequence of which is the assignment of an
initial state. By this action a fully automated dialog
between participant and system commences. This dia-
log is executed over a text message service and con-
sists of questions with pre-defined answer schemes.
Depending on the participants answer, state transitions
are triggered and states are changed, based on the
intervention flow formalized as rules, to lastly tailor the
follow-up communication between participant and sys-
It is just this exchange of information between partici-
pant and system over a longer period of time that we
want to utilize to personalize MobileCoach. The more
communication occurs and information is exchanged,
the more evidence we have to possibly derive a com-
prehensive picture of the participant’s needs. With re-
gard to the conceptual design described above, the
implications are that state transitions can no longer be
triggered by taking into account only the last answer of
the participant and the system’s current state. In fact,
in addition to the participants answer, the current state
and all the previous states have to be considered be-
fore a participant-specific behavioral health intervention
can be triggered. With this in mind, a personalized
MobileCoach would not only be able to recognize the
interventions that had the biggest impact on the inter-
vention progress of the participant, but also omit the
ones that had less and lastly determine the treatment
that is most efficient for the participant. Finally, differ-
ent forms of personalization have been used in different
fields. Among the most prominent, web search person-
Figure 1: In this picture we show
the coughing detection applica-
tion, which makes use of the
described algorithm. This applica-
tion also enables a recording
functionality, which was used in
the test.
alization, where the goal is to tailor search queries to a
particular individual, based on her interests and prefer-
ences [16]. With regard to mobile application, person-
alized training periods have been exploited to infer the
mood state of an user by analyzing communication
history and application usage patterns of a smartphone
The personalized cough detection module
for MobileCoach
As described in the previous section, personalization
should occur over time. For machine learning based
cough detection, this could look as follows: After each
night, the application detects and counts coughs and
possibly triggers DHI based on the amount of coughs.
Additionally, the application may choose a number of
recorded sound sequences, preferably sequences which
have been on the verge of being detected as cough
sounds or being ignored, respectively. These chosen
sequences are then queried to the participant to be
labeled as coughs or non-coughs respectively. Alterna-
tively, this data could also be judged and labeled by a
third party such as cough experts or on-demand work-
ers (e.g. [12]). Finally, by continuously integrating this
new labeled data in the model learning process, per-
sonalization is achieved. This special case of supervised
learning is known in literature as active learning and is
characterized by its queries [25].
The subject of the current work has been the develop-
ment of a coughing detection module for MobileCoach
to provide personalization as described above. The
developed module is based on classification of spectral
features from the audio signal of a smartphone’s mi-
crophone. The system pursues a similar implementation
as in [6]. However, support vector machine classifica-
tion was used instead of simple decision-tree classifica-
tion, which proved to generate better results in terms
of sensitivity, specificity and accuracy with respect to
our settings. A first test included a population of 5
healthy subjects (2 female, 3 male, mean age: 27, SD:
2.55), recorded by means of a bespoke app running on
Android 6.0 OS on a HTC M8 smartphone. In this first
test, 16 intentional coughs were recorded instead of
natural coughs. The participants were instructed to
intentionally cough while being recorded by the afore-
said smartphone. Analogously, the participant was
asked to read for a limited amount of time. These re-
cordings build the coughing data and non-coughing
data, from which a predicting model was learned. The
evaluation of the test yielded 86.7% sensitivity and
81.0% specificity, with an accuracy of 83.3%.
Future Work
We emphasized in this work the expendability of
MobileCoach towards personalization and a new sensing
module for smartphones. In the matter of asthma,
further sensing modules for MobileCoach can be
thought of as for instance, smartphone-based
spirometry. Spirometry is the mainstay for measuring
lung function and reports suggest that its functionality,
namely the measurement of instantaneous flow and
cumulative volume of exhaled air, may be reproducable
by means of a mobile phone [11, 18]. Even beyond
sensing, we can consider supportive DHIs. For
example, wind instruments have been used in music
therapy to improve the skill of asthmatic patients to
attack asthma [14]. This finding can potentially be
exploited by a smartphone-based wind instrument
application [26] and be embedded in a behavioral DHI.
Against this background, we want to extend
MobileCoach with various sensing and supporting DHIs
in our future work: not only to provide a broader tool-
set to intervention experts, but also to design efficient
and effective interventions, which are tailored to the
needs of the participants and provide objective
(physiological) data on therapy adherence.
We would like to thank Gabriella Chiesa and Niklas
Elser of the CSS Health Lab for their valuable feedback.
This work is part-funded by CSS Insurance, Switzer-
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... Besides diabetes, ML-driven adaptive systems were also developed to manage other diseases. For instance, the MobileCoach application (Barata et al., 2016) supports asthmatic patients by detecting coughs (via sounds obtained from their smartphone's microphone) using an SVM model (acc ¼ 83.3%) and then providing personalized intervention to the patient based on the amount of coughs. Similarly, to support patients who suffered a cardiac event and are in Phase III of the recovery process, Prabhu et al. (2018) developed the MedFit system to recognize up to 14 local muscular endurance (LME) exercises completed by the patient using a high-performing SVM classifier (acc ! ...
... Barata et al., 2016;Kariyawasam et al., 2019), images (Chin et al., 2020; Rabbi et al., 2015; Rachakonda et al., 2020), emotion/mood log ...
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... Tailoring behavioural intervention to patients need have been extensively studied in the literature. For example, health coaching intervention through messaging app have been used to tailor the treatment and enhance the capability of the system to provide digital health interventions [23]. Kowatsch et al., [24] developed MobileCoach (MC), a fully automated behaviour change intervention to improve health behaviours. ...
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Laut WHO sind chronische Krankheiten wie Herz-Kreislauf-Erkrankungen, Krebs, Diabetes oder Asthma weltweit für circa 70 % aller Todesfälle verantwortlich. Leistungserbringer haben allerdings nur beschränkte Ressourcen und können den Gesundheitszustand im Alltag von Patienten nicht kontinuierlich erheben und daher auch nicht immer rechtzeitig intervenieren, bevor es zu einer allfälligen Hospitalisierung kommt. Vor diesem Hintergrund diskutiert dieser interdisziplinäre Beitrag das Potenzial digitaler Pillen. Das Ziel digitaler Pillen besteht darin, Gesundheitszustände mithilfe von Informations- und Kommunikationstechnologie möglichst kontinuierlich, zweckdienlich und bequem zu erheben und nur dann zu intervenieren, wenn es unbedingt sein muss, kurzum Patienten den Umgang mit ihrer chronischen Krankheit im Alltag zu erleichtern. Nach einer Einleitung wird das Konzept digitaler Pillen näher erläutert. Danach werden fünf digitale Pillen aus den Bereichen Gesundheitskompetenz, Prävention und Therapie näher vorgestellt. Abschließend wird das Konzept der digitalen Pille kritisch reflektiert und Potenziale sowie Herausforderungen werden diskutiert.
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Introduction: Nocturnal cough is a burdensome asthma symptom. However, knowledge about the prevalence of nocturnal cough in asthma is limited. Furthermore, prior research has shown that nocturnal cough and impaired sleep quality are associated with asthma control, but the association between these two symptoms remains unclear. This study further investigates the potential of these symptoms as markers for asthma control and the accuracy of automated, smartphone-based passive monitoring for nocturnal cough detection and sleep quality assessment. Methods and analysis: The study is a multicentre, longitudinal observational study with two stages. Sensor and questionnaire data of 94 individuals with asthma will be recorded for 28 nights by means of a smartphone. On the first and the last study day, a participant’s asthma will be clinically assessed, including spirometry and fractionated exhaled nitric oxide levels. Asthma control will be assessed by the Asthma Control Test and sleep quality by means of the Pittsburgh Sleep Quality Index. In addition, nocturnal coughs from smartphone microphone recordings will be labelled and counted by human annotators. Relatively unrestrictive eligibility criteria for study participation are set to support external validity of study results. Analysis of the first stage is concerned with the prevalence and trends of nocturnal cough and the accuracies of smartphone-based automated detection of nocturnal cough and sleep quality. In the second stage, patient-reported asthma control will be predicted in a mixed effects regression model with nocturnal cough frequencies and sleep quality of past nights as the main predictors. Ethics and dissemination: The study was reviewed and approved by the ethics commission responsible for research involving humans in eastern Switzerland (BASEC ID: 2017–01872). All study data will be anonymised on study termination. Results will be published in medical and technical peer-reviewed journals.
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We are investigating to which degree of accuracy can a mobile application detect asthmatic nocturnal cough and sleep quality with the smartphone’s built-in microphone?
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Background: Cough is recognised as an important troublesome symptom in the diagnosis and monitoring of asthma. Asthma control is thought to be determined by the degree of airway inflammation and hyper-responsiveness but how these relate to cough frequency is unclear. Objective: To investigate the relationships between objective cough frequency, disease control, airflow obstruction and airway inflammation in asthma. Methods: Participants with asthma underwent 24 hour ambulatory cough monitoring, exhaled nitric oxide, spirometry, methacholine challenge and sputum induction (cell counts and inflammatory mediator levels). Asthma control was assessed by GINA classification and the Asthma Control Questionnaire (ACQ). Results: Eighty-nine subjects with asthma (mean age 57 years (±SD 12); 57% female) were recruited. According to GINA criteria, 18 (20.2%) patients were classified as controlled, 39 (43.8%) partly controlled and 32 (36%) uncontrolled; median (range) ACQ score was 1 (0.0-4.4). ACQ-6 correlated with 24hr cough frequency (r=0.40; p<0.001) and patients with uncontrolled asthma (GINA) had higher median 24hr cough frequency (4.2c/h, range 0.3-27.6) compared with partially controlled and controlled asthma (1.8c/h, range 0.2-25.3 and 1.7c/h range 0.3-6.7, p=0.01 and p=0.002 respectively). Measures of airway inflammation were not significantly different between GINA categories and were not correlated with ACQ. In multivariate analyses, increasing cough frequency and worsening FEV1 independently predicted measures of asthma control. Conclusion: Ambulatory cough frequency monitoring provides an objective assessment of asthma symptoms that correlates with standard measures of asthma control, but not airflow obstruction or airway inflammation. Moreover, cough frequency and airflow obstruction represent independent dimensions of asthma control.
Conference Paper
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Information generated within the World Wide Web is increasing at huge rate and user's access for their own interested work. Ambiguous queries and lower ability of user to precisely express what they need have been one of the challenging obstacles in improving search results. It is obvious that the current search engines retrieve results are sometimes not of user relevance due to keyword based search, so to fill the gap between the user interest and retrieved search results, personalized web search needs to be evolved. For example, a biologist and a programmer may use the same query “mouse” with different search context, but the search systems would return same results. Again still there is need to customize the search results by re-ranking the retrieved results incorporating the user interests. In this paper we have presents various approaches to personalize web search through user modelling by analyzing semantic data.
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We systematically reviewed randomized controlled trials (RCTs) assessing the effectiveness of computerized decision support systems (CDSSs) featuring rule- or algorithm-based software integrated with electronic health records (EHRs) and evidence-based knowledge. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and Cochrane Database of Abstracts of Reviews of Effects. Information on system design, capabilities, acquisition, implementation context, and effects on mortality, morbidity, and economic outcomes were extracted. Twenty-eight RCTs were included. CDSS use did not affect mortality (16 trials, 37395 patients; 2282 deaths; risk ratio [RR] = 0.96; 95% confidence interval [CI] = 0.85, 1.08; I ² = 41%). A statistically significant effect was evident in the prevention of morbidity, any disease (9 RCTs; 13868 patients; RR = 0.82; 95% CI = 0.68, 0.99; I ² = 64%), but selective outcome reporting or publication bias cannot be excluded. We observed differences for costs and health service utilization, although these were often small in magnitude. Across clinical settings, new generation CDSSs integrated with EHRs do not affect mortality and might moderately improve morbidity outcomes.
Conference Paper
This paper presents an efficient cough detection system based on simple decision-tree classification of spectral features from a smartphone audio signal. Preliminary evaluation on voluntary coughs shows that the system can achieve 98% sensitivity and 97.13% specificity when the audio signal is sampled at full rate. With this baseline system, we study possible efficiency optimisations by evaluating the effect of downsampling below the Nyquist rate and how the system performance at low sampling frequencies can be improved by incorporating compressive sensing reconstruction schemes. Our results show that undersampling down to 400 Hz can still keep sensitivity and specificity values above 90% despite of aliasing. Furthermore, the sparsity of cough signals in the time domain allows keeping performance figures close to 90% when sampling at 100 Hz using compressive sensing schemes.
Conference Paper
Cost and accessibility have impeded the adoption of spirometers (devices that measure lung function) outside clinical settings, especially in low-resource environments. Prior work, called SpiroSmart, used a smartphone's built-in microphone as a spirometer. However, individuals in low- or middle-income countries do not typically have access to the latest smartphones. In this paper, we investigate how spirometry can be performed from any phone-using the standard telephony voice channel to transmit the sound of the spirometry effort. We also investigate how using a 3D printed vortex whistle can affect the accuracy of common spirometry measures and mitigate usability challenges. Our system, coined SpiroCall, was evaluated with 50 participants against two gold standard medical spirometers. We conclude that SpiroCall has an acceptable mean error with or without a whistle for performing spirometry, and advantages of each are discussed.
Effective and efficient behavioral interventions are important and of high interest today. Due to shortcomings of related approaches, we introduce MobileCoach ( as novel open source behavioral intervention platform. With its modular architecture, its rule-based engine that monitors behavioral states and triggers state transitions, we assume MobileCoach to lay a fruitful ground for evidence-based, scalable and low-cost behavioral interventions in various application domains. The code basis is made open source and thus, MobileCoach can be used and revised not only by interdisciplinary research teams but also by public bodies or business organizations without any legal constraints. Technical details of the platform are presented as well as preliminary empirical findings regarding the acceptance of one particular intervention in the public health context. Future work will integrate Internet of Things services that sense and process data streams in a way that MobileCoach interventions can be further tailored to the needs and characteristics of individual participants.
Conference Paper
This study discusses the concept of personalization and customization mechanism performed specifically for a cloud based vehicular platform. The proof-of-concept prototype allows the user to create a personalized environment on the Vehicular On-Board Unit (OBU) from hand-held devices of vehicular users. The paper discusses an extensive analysis on different type of vehicular users using different type of services, methodologies to use intermediary component between the vehicle and service providers in-order to personalize OBU. For this purpose, we have defined methodologies both for the service providers and vehicular users. For users, we have enabled peer-to-peer application layer personalization including the module of object storage on cloud. For service providers we have illustrated the idea of an engine allowing them to orchestrate services with respective to the user-types. Although it was implemented as a proof-of-concept system but it can be easily applicable for various users, service providers and hence can be extended further.
Home spirometry is gaining acceptance in the medical community because of its ability to detect pulmonary exacerbations and improve outcomes of chronic lung ailments. However, cost and usability are significant barriers to its widespread adoption. To this end, we present SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone. We evaluate SpiroSmart on 52 subjects, showing that the mean error when compared to a clinical spirometer is 5.1% for common measures of lung function. Finally, we show that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.