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Prevalence of nocturnal cough in asthma and its potential as a marker for asthma control (MAC) in combination with sleep quality: Protocol of a smartphone-based, multicentre, longitudinal observational study with two stages

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  • Resmonics AG

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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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1
TinschertP, etal. BMJ Open 2019;9:e026323. doi:10.1136/bmjopen-2018-026323
Open access
Prevalence of nocturnal cough in asthma
and its potential as a marker for asthma
control (MAC) in combination with
sleep quality: protocol of a smartphone-
based, multicentre, longitudinal
observational study with two stages
Peter Tinschert,1 Frank Rassouli,2 Filipe Barata,3 Claudia Steurer-Stey,4,5
Elgar Fleisch,1,3 Milo Alan Puhan,4 Martin Brutsche,2 Tobias Kowatsch1
To cite: TinschertP, RassouliF,
BarataF, etal. Prevalence of
nocturnal cough in asthma
and its potential as a marker
for asthma control (MAC) in
combination with sleep quality:
protocol of a smartphone-
based, multicentre, longitudinal
observational study with
two stages. BMJ Open
2019;9:e026323. doi:10.1136/
bmjopen-2018-026323
Prepublication history for
this paper is available online.
To view these les, please visit
the journal online (http:// dx. doi.
org/ 10. 1136/ bmjopen- 2018-
026323).
Received 27 August 2018
Revised 5 November 2018
Accepted 16 November 2018
For numbered afliations see
end of article.
Correspondence to
PeterTinschert;
peter. tinschert@ unisg. ch
Protocol
© Author(s) (or their
employer(s)) 2019. Re-use
permitted under CC BY-NC. No
commercial re-use. See rights
and permissions. Published by
BMJ.
ABSTRACT
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 rst 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 rst 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.
Trial registration number NCT03635710; Pre-results.
INTRODUCTION
Asthma, a chronic respiratory disease, is
one of the most prevalent chronic condi-
tions. According to the WHO, 235 million
people suffer from asthma with 383 000 asth-
ma-related deaths in 20151. Common symp-
toms include wheezing, shortness of breath,
chest tightness and coughing.2 Coughing
is perceived by patients as a troublesome
symptom,3 predicts asthma severity4 and indi-
cates a worse prognosis.5
In asthma, coughing and other symp-
toms tend to get worse at night and often
cause awakenings.2 It has been shown that
‘asthma control’, the extent to which asthma
Strengths and limitations of this study
The planned study has the potential to generate
a novel, insightful and externally valid dataset for
studying nocturnal symptoms in asthma.
Nocturnal cough and sleep quality may be valid
markers for asthma control, which could help to
identify windows of opportunities for pharmacologi-
cal and non-pharmacological interventions.
In principle, smartphones are powerful devices for
the automatic monitoring of asthma symptoms, and
this study will provide evidence whether symptom
detection accuracies actually meet clinical require-
ments under real-life conditions.
While the study duration of 29 days per participant
allows for the investigation of prevalence and trends
in nocturnal cough and sleep quality, it is not suf-
cient to account for long-term symptom patterns and
trends (eg, for seasonal effects in allergic asthma).
Due to the heterogeneity of asthma, the potential
clinical usefulness of the investigated markers may
differ considerably between subgroups of individu-
als with asthma.
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symptoms are controlled by treatment,2 is statistically asso-
ciated with sleep quality.6 While the association between
sleep quality and asthma control is well established, the
role of nocturnal cough is less clear.
A first cross-sectional study has indicated that nocturnal
cough frequency might be a valid marker for asthma
control,7 rendering it a potentially useful parameter for
disease self-monitoring. However, due to the design of the
above-mentioned study, questions regarding longitudinal
trends of nocturnal cough in asthma remain unanswered.
It is also unclear whether nocturnal cough frequency
holds any value for asthma control prediction. Moreover,
predictions of asthma control on subsequent days based
on nocturnal cough frequency could be independent of,
moderated by or mediated through sleep quality.
The ongoing study addresses these research gaps
by investigating the prevalence of nocturnal cough in
asthma over the course of 4 weeks and by exploring the
interplay of nocturnal cough frequency and sleep quality
for asthma control levels on subsequent days. In this way,
the study could indicate whether nocturnal cough and
sleep quality are useful parameters for signalling windows
of opportunity during which timely pharmacological and
non-pharmacological interventions could be adminis-
tered to prevent asthma deteriorations and attacks.
In addition to these medical objectives, the study aims
to investigate the potential of commercial smartphones
in the automatic detection of nocturnal cough and assess-
ment of sleep quality. Enabled by the recent progress in
the domain of machine learning-based symptom detec-
tion,8 9 self-learning algorithms will be applied on the
study’s smartphone sensor data to develop models for
detecting and assessing the above-mentioned symptoms.
The ongoing study explores whether these models are
sufficiently accurate for potential medical applications.
METHODS AND ANALYSIS
The study is designed as a multicentre, longitudinal
observational study with two stages. The study stages
are identical in terms of included participants and data
collection procedure. However, they differ in terms of
objectives, endpoints and statistical analysis: the first stage
is mainly concerned with descriptive analysis of nocturnal
cough, while the second stage focuses on the prediction
of asthma control based on nocturnal cough frequency
and sleep quality.
Aims and objectives
The study has three objectives.
Objectives of the rst stage
In the first study stage, the prevalence of nocturnal cough
and trends in nocturnal cough frequency will be explored
descriptively (1). As a second objective, the study data
will be used to develop and evaluate machine learning
models for nocturnal cough detection and sleep quality
assessment (2).
Objectives of the second stage
In the second stage, the objective is to examine nocturnal
cough frequency as a marker for asthma control in combi-
nation with sleep quality (3). In other words, the findings
of Marsden et al7 will be expanded by collecting longitu-
dinal data, adding sleep quality to the prediction model,
measuring additional outcomes such as asthma quality of
life and cough status and accounting for environmental
factors such as weather conditions, pollution and outside
temperature.
Study setting
The study duration per participant will be 29 days with a
3-day run-in phase from the second to fourth study day.
Two study centres in Switzerland are participating in the
ongoing study: the Clinic for Pulmonology and Sleep
Medicine of the Cantonal Hospital St. Gallen and the
mediX Group practice Zurich in cooperation with the
University of Zurich. On the first and last day of the study,
participants will have an in-person appointment at one
of the study centres. In the time between appointments,
patient-reported outcomes and nocturnal sensor data will
be collected by means of a smartphone.
The first participant was included in February 2018.
At the time of manuscript submission, 37 participants
have completed the study. Data collection is expected to
conclude in the first quarter of 2019.
Eligibility criteria
The population investigated in this study are adults with
asthma. Inclusion criteria are not restrictive to ensure
that findings are generalisable to the target population of
adults with asthma: participants must have self-reported
physician-diagnosed asthma, be 18 years or older, provide
written informed consent and know how to operate a
smartphone (self-reported in a yes–no question).
Participants, to whom the following criteria apply, are
excluded from participation: participants who are unlikely
to successfully complete the daily study tasks on a regular
basis (ie, participants with cognitive impairments due to
diseases such as severe depression, dementia and Alzhei-
mer’s disease), participants for whom obtaining reliable
nocturnal measurements is not feasible due to the study’s
rule-based smartphone recording system (ie, participants
with severe insomnia and workers with varying day shift
and night shifts) and participants for whom a solely
acoustic-based allocation of nocturnal coughs is highly
error prone (ie, participants who share the bedroom with
a person from the same sex). For a more differentiated
rationale behind the latter exclusion criterion, please
refer to the discussion.
Outcomes
An overview of all study measures is presented in table 1
in the data collection section.
Endpoints of the rst stage
The primary endpoint is coughs per night (c/n), obtained
through smartphone audio-recordings, manually
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labelled by trained human annotators. Adhering to the
recommendations of the European Respiratory Society,
cough is defined in this study as ‘a three-phase expulsive
motor act characterized by an inspiratory effort (inspi-
ratory phase), followed by a forced expiratory effort
against a closed glottis (compressive phase) and then
Table 1 Overview of measures throughout the 29-day study duration
Frame of reference Time of measurement Obtained by
Patient-reported outcomes
Medical questionnaires
ACT Last 4 weeks d1, d29 Physician*
ACT Last week† d8, d15, d22, d29 Smartphone app
PSQI Last 4 weeks d29 Study nurse*
PSQI Last night† d1 – d29 Smartphone app
AQ20 Momentary d1, d29 Smartphone app‡
LCQ Last 2 weeks d1, d29 Smartphone app‡
VAS (severity of nocturnal cough) Last night d1 – d29 Smartphone app
VAS (severity of cough by day) Last day d1 – d29 Smartphone app
VAS (asthma symptom strength) This morning d1 – d29 Smartphone app
Other questionnaires
Usage habit smartphone app Momentary d8, d15, d22, d29 Smartphone app
Technical evaluation questionnaire Last 4 weeks d29 Study nurse
Medical control questions
Occurrence of cold/rhinitis Last week d8, d15, d22, d29 Smartphone app
Asthma related events (exacerbationsand physician
visits)
Last week d8, d15, d22, d29 Smartphone app
Technical control questions
Smartphone position overnight Last days d3, d14, d24 Smartphone app
Smartphone distance overnight Last days d3, d14, d24 Smartphone app
Objective measures
Lung function assessments
FEV1d1, d29 Spirometry
FVC d1, d29 Spirometry
FEV1/FVC d1, d29 Spirometry
FeNO d1, d29 NIOX handheld device
Smartphone data
Microphone n1 – n28§ Smartphone app
Accelerometer n1 – n28§ Smartphone app
Proximity sensor n1 – n28§ Smartphone app
Ambient light sensor n1 – n28§ Smartphone app
GPS n1 – n28§ Smartphone app
Bluetooth n1 – n28§ Smartphone app
Usage data (screen lock status, connection statusand
battery status)
n1 – n28§ Smartphone app
*In case of non-resolvable appointment scheduling issues on day 29, the participant is instructed to ll out an online version of the
questionnaire to ensure parallel measurements between participants.
†The temporal frame of reference for these questionnaires is shortened in comparison with the respective original publication to
produce more distinct measurements for each participant, thereby increasing the data resolution for statistical modelling. The
question framing and answer options are adapted to the revised frame of reference.
‡If a participant does not respond to the smartphone app questionnaire in the morning of day 29, the questionnaire will be
administered by the study nurse during the participant’s study centre visit.
§n1 is the night between day 1 (d1) and day 2 (d2) and so forth.
ACT, Asthma Control Test; AQ20,Asthma Questionnaire 20; FeNO, fractionated exhaled nitric oxide; FEV1,forced expiratory volume
in 1s; FVC, forced vital capacity; GPS, global positioning system; LCQ, Leicester Cough Questionnaire; PSQI, Pittsburgh Sleep
Quality Index; VAS, visual analogue scale.
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by opening of the glottis and rapid expiratory airflow
(expulsive phase)’.10
Secondary endpoints are cough-specific health status
(eg, the presence and severity of cough and its effects
on quality of life, measured twice by the Leicester Cough
Questionnaire11 and daily by a visual analogue scale for
cough severity12) and sleep quality as measured by the
Pittsburgh Sleep Quality Index (PSQI).13
Endpoints of the second stage
In the second stage, the primary endpoint is patient-re-
ported asthma control measured by the Asthma Control
Test (ACT).14
The secondary endpoint of this stage is asth-
ma-specific quality of life, as measured by the Asthma
Questionnaire-20.15
Participant timeline
The study flow chart from a participant’s perspective is
illustrated in figure 1. Participants have to attend two
study centre visits at the beginning (d1) and end (d29) of
the study. Furthermore, participants have to adhere fully
to the protocol of the smartphone-based remote data
collection phase (d1–d28) on at least 23 days. Otherwise,
they will be withdrawn from the study. More specifically,
participants are instructed to keep the study smartphone
in their bedroom at night and ensure that the smartphone
either has a battery level of at least 80% or recharges at
night. Participants are also asked to complete the daily
questionnaire (preferably in the morning but before
21:00 at the latest).
The length of the daily questionnaire differs between
days. On 21 of 29 days, participants have to answer 13
questions (approximately 4 min effort). On three occa-
sions, participants will receive 15 questions (d3, d14, d24;
5 min effort). At the end of each study week, participants
are asked to fill out an extended questionnaire with 43
questions (d8, d15, d22; 15 min effort). Most questionnaires
on the first and last study day will also be administered by
means of the smartphone (ie, 52 questions on d1 and 62
questions on d29 with 20 and 25 min effort, respectively).
Please refer to the data collection section for a detailed
description of the questionnaire schedule.
Sample size
The study is powered with regard to the primary endpoint
of the second stage. The required sample size was calcu-
lated based on the results of Marsden et al.7 That is, a
correlation of r=0.30 between nocturnal cough frequency
and asthma control was set as the expected strength of
correlation. In order to ensure a power (1-ß) of 80%
with a two-tailed type 1-error probability (α) of 5% for
rejecting a zero bivariate correlation,16 85 participants
Figure 1 Study owchart from the participant’s perspective. Please note that more detailed information on the assessments
and measurements is provided in the measures subsection of the data collection section. AC20, Asthma Questionnaire-20; ACT,
Asthma Control Test; FeNO,fractionated exhaled nitric oxide; LCQ, Leicester Cough Questionnaire; PSQI, Pittsburgh Sleep
Quality Index; VAS, visual analogue scales).
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are required. In terms of parameter accuracy estima-
tion,17 this sample size would result in a 95% CI for the
above-mentioned correlation of [0.09, 0.48].
A dropout rate of 10% is assumed, thus 94 participants
will be recruited. To the best knowledge of the authors, no
research that investigated the relationship of nocturnal
cough, sleep quality and asthma control in a longitudinal
setting is available hitherto. This prohibits a more precise
evidence-based estimation of the required sample size.
Consequently, the required sample size calculation does
not take sleep quality and the longitudinal data structure
into account. However, due to the comparable strength
of association between sleep quality and asthma control
(r=0.31–0.36)6 and nocturnal cough and asthma control
(r=0.30),7 the required sample size holds for this relation-
ship, too.
Recruitment
Personal and impersonal recruitment is employed.
Eligible participants from the participating study centres
will be referred directly to the study nurse. Multiple chan-
nels and methods are used for impersonal recruitment:
emails are sent out to mailing lists from Swiss participant
organisations for lung diseases and nearby universities,
flyers are displayed in local medical offices, pharmacies
and universities and online ads are posted to social media
websites and digital bulletin boards. Additionally, an
email is sent out to clients from a Swiss health insurance
provider who have submitted at least one asthma medica-
tion receipt for reimbursement in the last 6 months. Please
note that no personal information of any participant will
be disclosed to the insurance provider. Only aggregated
data are made available to the insurance provider (eg, the
presumptive number of clients who have responded to
mail recruitment).
The recruitment material contains a link to an online
screening survey, which consists of the participant infor-
mation form, a video introducing the study and a short
screening questionnaire to test for study eligibility. If
potential participants state their interest for study partici-
pation and pass the screening survey, their contact details
are sent to the study nurses for appointment scheduling.
Participants do not receive monetary incentives for
study participation. However, travelling costs are reim-
bursed up to SFr50 and a lottery will be held on study
termination, with 10 prizes worth SFr300 each. Further-
more, a personalised summary of patient-reported study
data is made available to each participant after successful
study participation.
Data collection
Methods
Health professionals will perform medical assessments in
the study centres on the first and last day of the study. For
the time between physician appointments, participants
will be equipped with a smartphone (Samsung Galaxy A3
2017, SM-A320FL) on which ‘Clara’, the chat-based study
app, is installed. This app is a study-specific adaption of
the mobile app18 for the open source behavioural inter-
vention platform MobileCoach ( www. mobile- coach. eu).19
It records a participant’s sensor data in the night (eg,
audio data via a smartphone’s microphone) and delivers
the daily questionnaires to the patients. Figure 2 illus-
trates the app’s user experience. More information on
the app is provided in the retention section.
Measures
Table 1 summarises the measures including temporal
framing for patient-reported outcomes, time of measure-
ment and by which means they are obtained (eg, medical
personnel or by the mobile app). All smartphone
measures are collected on the study smartphone handed
out to participants on the first study day. Measures can
be divided into patient-reported outcomes (eg, medical
questionnaires) and objective measures (ie, lung func-
tion assessments and nocturnal smartphone sensor data).
Nocturnal smartphone sensor data are recorded by
default from 23:00 onwards for a recording length of
9 hours and 40 min to account for the sleeping patterns
of the general Swiss population (ie, inferring from a
citizen science study,20 95% of nocturnal sleep sessions
are shorter than this default recording length). Further-
more, participants can manually start (after 21:00) and
stop (after 04:00) the sensor data recording, thereby
overwriting the default recording settings. If a partic-
ipant starts the recording manually after the recording
has already been started automatically (ie, after 23:00),
the default recording length is extended so that it encom-
passes 9 hours and 40 min from the moment of manual
initiation.
In addition to the measures in table 1, a comprehensive
medical assessment of a participant’s asthma is performed
on the first study day. The following medical parame-
ters are determined in this assessment: asthma type (eg,
allergic/non allergic asthma, asthma and obesity), asthma
history (eg, onset, asthma symptoms and exacerbations in
the past), asthma severity and control (by means of Global
Initiative for Asthma (GINA) stage2), asthma medication
that is prescribed and taken and smoking behaviour.
The medical assessment on the last study day examines
whether changes in the above-mentioned parameters
occurred during the course of the study.
Retention
Adherence to the data collection protocol is promoted
through multiple strategies. First, participants meet with
the study nurse during their first study centre visit to
discuss how they can implement the instructions in their
daily life and overcome possible barriers. Second, the
study app contains a reminder system. If participants do
not interact with the study app (and thus fail to comply
with their study tasks), they will receive an SMS reminder
to their private smartphone. If non-adherence occurs for
two consecutive days, the participant receives a reminder
phone call from the study nurse. Third, the study app is
specifically designed to promote participant retention.
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Participants are guided through the remote data collec-
tion phase of the study by a text-based healthcare chatbot.21
More specifically, all study information, instructions and
tasks are conveyed in a daily conversation with ‘Clara’, a
virtual study nurse designed to establish a working alliance
with the participant.22 This interactive approach is known
to increase adherence in clinical settings.23 Furthermore,
several nudging techniques24 are incorporated in the app
(figure 2 shows how the study makes use of loss aversion
in participants to increase engagement, ie, by symbolising
non-adherence as lost hearts).
Data management
All participant data gathered by the physician (eg, results
of the asthma assessment) and study nurse (eg, lung func-
tion assessments) in the study centres will be transferred
into an electronic format and stored online on the study
server. Traceability of changes and version control will
be managed by the version control software TortoiseSVN
(https:// tortoisesvn. net/).
Questionnaire data collected by the mobile app will be
instantly saved on the study server. Nocturnal sensor data
will be stored locally on the smartphone and backed up
to external hard drives and secure online storage once a
participant has completed the study. However, excerpts of
nocturnal sensor data are uploaded continuously to the
study server for quality monitoring purposes. ETH Zurich
provides all study servers and online storage services used
in the study.
To match different data sources, a unique user hash is
randomly generated for data obtained by smartphone
application for each participant, and then linked to the
participant ID used in the study centres’ case report
forms. Please note that a participant’s personal infor-
mation is kept exclusively at the study centres in a sepa-
rate document (ie, it is not included in the case report
forms). Once the data analysis is complete, the docu-
ment containing a participant’s personal information
is destroyed to anonymise the study data.
Analysis
Data preparation
In order to obtain c/n, which is the primary endpoint of
the first stage and a fundamental part of the analysis in
the second stage, the raw audio data have to be annotated
for cough events. Due to the study setting, human anno-
tators will label cough exclusively based on audio data.
Every annotator will receive labelling training before
being granted access to the audio data.
Annotators will be asked to label the explosive phase of
a cough and, if multiple coughs occur in succession, the
corresponding cough epoch10 (see figure 3 for an illustra-
tive example for the labelling). By storing the meta-infor-
mation of the cough labels (ie, time at which the cough
occurred in a given night and cough duration), the time
spend coughing per night can be calculated (also referred
to as ‘cough seconds’). In this way, the study’s labelling
process covers three out of the four ways to quantitatively
Figure 2 Screenshots of the ‘Clara’ study app. Sidebar for navigation (left), chat-based interface with predened answer
options (middle)and questionnaire module for patient-reported outcomes (right).
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describe coughing that are proposed by the European
Respiratory Society.10
Furthermore, annotators will be asked to label the
sex of the person for each cough. If uncertainties occur
during the labelling process (eg, unclear sex of a cougher
or whether a sound was a cough), annotators will mark
the corresponding section for review. A subset of the
recorded nights will be labelled by all annotators, so that
inter-rater agreement can be estimated.
For subsets of audio data where no human speech
is included, coughs may be labelled by means of a
crowdsourcing approach (eg, Amazon Mechanical
Turk; https://www. mturk. com/).
Planned statistical analysis in the rst stage
In the first study stage, c/n will be analysed descriptively
and visually. Additionally, autoregressive and moving
average models will be used to model trends in c/n over
multiple nights.
Additionally, machine-learning models will be devel-
oped to: (1) automatically detect nocturnal cough and
(2) assess sleep quality based on the smartphone sensor
data. For (1), manually annotated coughs constitute the
ground truth and parameters obtained from the smart-
phone audio-recordings serve as algorithm features.
For (2), PSQI scores are the ground truth and all avail-
able smartphone sensor data may be used as algorithm
features. Both algorithms will be designed using super-
vised machine learning techniques and evaluated in accor-
dance with standard practice of algorithm development.25
Planned statistical analysis in the second stage
In the second stage, a mixed effects model will be esti-
mated to analyse the diagnostic efficacy of nocturnal
cough and sleep quality as markers for asthma control
and asthma-related quality of life. The mixed effects
model is selected to account for the nested data struc-
ture (ie, c/n is nested in participants). Asthma control,
as measured by the ACT, will be the predicted outcome.
In a second model, asthma-related quality of life as
measured by the AC20 will be used as the outcome vari-
able instead. Predictor variables in both models will be
c/n, PSQI scores and the interaction of c/n and PSQI
scores. The models will take multiple control variables
into account (eg, type of asthma, medication intake,
concomitant diseases and smoking behaviour). By model-
ling the data with a random intercept and random slope,
the heterogeneous manifestation of asthma symptoms
in participants will be represented in the models (eg,
it seems plausible that c/n might have a higher predic-
tive power for asthma control and/or quality of life for
some participants; compare cough-variant asthma26). In
order to account for repeated measurements, the statis-
tical models will rely on an autoregressive covariance
structure.27
In a further exploratory analysis, a modified version
of the asthma control mixed effects prediction model
will estimated in which the outputs of the two machine
learning models will serve as the predictors instead of
the corresponding ground truths (ie, manually labelled
coughs and PSQI scores as described in the last para-
graph). The prediction performance of the original and
modified model version will be compared so that the
utility of a machine learning based symptom detection
approach can be evaluated.
Please note that since the present study is rather explor-
ative in its nature, deviations from the planned statistical
analysis may be warranted based on the structure and
distributions of the collected data.
Patient and public involvement
The development of the research objectives was
informed by two focus groups with adults with asthma.
The 15 participants were recruited by means of purpo-
sive sampling, ensuring that patients with uncontrolled,
partially controlled and well-controlled asthma from a
wide age range (21–66 years) participated in the discus-
sions. Patients voiced the need for a monitoring method
with objective and unobtrusive symptom measurements.
Furthermore, passive nocturnal symptom monitoring
by means of a smartphone was regarded a promising
approach to satisfy this need.
Figure 3 Labelling example for cough explosive phases and epochs. Screenshot taken in Audacity 2.2.2. (https://www.
audacityteam.org).
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Patients were not directly involved in the design of
the study. However, insights from the focus groups have
informed the design of the study app.
Study participants will receive a personalised summary
of the collected patient-reported study data after
successful study completion and can opt-in to receive the
study results once they have been published.
ETHICS ANDDISSEMINATION
Consent
Participants will provide informed written consent at
their first visit of a study centre. Participants can withdraw
their consent at any time during the study.
Access to data and condentiality
Only the study team (ie, the study nurses, involved physi-
cians and technical support staff) will have access to the
study data before anonymisation. Smartphone sensor data
will be stored separately from private participant informa-
tion obtained in the case report form (CRFs). After the
study is completed, all participant data will be matched,
and the physical files containing the private informa-
tion will be destroyed. Furthermore, all audio segments
containing a participant’s speech will be erased with the
objective to anonymise all study data. For anonymised
audio data, the cough labelling process may be scaled by
means of a crowdsourcing approach.
Dissemination policy
Results of the study are planned to be published in
medical and technical peer-reviewed journals. Consis-
tent with the Open Research Data initiative of the Swiss
National Science Foundation,28 the study team may
publish the anonymised study data in a scientific data-
base if participants provide informed consent and all the
applicable legal provisions are met. CSS insurance, the
external funder of the study, has no active involvement in
the study as stated in the funding contracts, for example,
in terms of study design, data analysis or the decision to
publish results.
DISCUSSION
The study follows three main objectives: (1) to ascertain
the prevalence and trends of nocturnal cough in asthma;
(2) to develop and evaluate smartphone-based machine
learning models for automated nocturnal cough detec-
tion and sleep quality assessment; and (3) to investigate
the potential of nocturnal cough frequency as a marker
for asthma control in combination with sleep quality.
The study is characterised by the volume, variety and
ecological validity of the collected data. Studies in the
domain of nocturnal respiratory symptoms are often
restricted to cross-sectional data,6 7 or only consider
limited data sources.29 They often rely exclusively on
survey data30–32 or collect data in artificial settings, such as
using obtrusive devices for cough detection.33–35 External
validity is further supported by the modest eligibility
criteria for study participation: from a medical perspec-
tive, participants are only excluded if they suffer from
a condition that renders successful study participation
unlikely. Therefore, study results should be generalisable
to the general population, which is often not the case in
asthma-related trials.36
However, external and internal validity constitute
conflicting priorities in study design.37 The study’s
emphasis on external validity results in a threat to internal
validity: in order to enable in situ measurements with a
high temporal resolution, the study relies considerably
on patient-reported outcomes and a purely sound-based
cough annotation approach.
To mitigate constraints of patient-reported outcomes,
participants will have a run-in phase of 3 days at the begin-
ning of the study, including a training session on the first
day, when they fill out daily recurring questions under
supervision of the study nurse.
To enable accurate annotation of coughs, a participant’s
acoustic fingerprint will be collected on the first study day
by recording intentional coughs and standardised voice
samples. Furthermore, participants will indicate every
morning whether they have slept alone or shared the
bedroom with another person or a pet. By providing addi-
tional information to human cough annotators, accuracy
of the labelling process is expected to increase. More-
over, an internal unpublished analysis conducted with
data from an earlier study38 has indicated that humans
are able to assign coughs correctly to a person’s sex only
by means of sound. In this analysis, six annotators, who
have not received labelling training or information about
the participants, assigned 53 lab-recorded coughs to the
correct sex with an average Cohen’s kappa of 0.92 (inter-
pretable as an almost perfect agreement39) after being
allowed to listen to each cough only once. This finding is
in line with prior research on sex differences in cough40 41
and suggests that human annotators are in principle able
to exploit sex differences in cough sounds to accurately
match coughs to participants. Therefore, the study’s
exclusion of participants who share their bedroom with a
person from the same sex is a further measure to enable
a reasonably accurate labelling process.
Furthermore, participants regularly specify the smart-
phone’s position and average distance to the bed at night.
This information serves as a quality control measure for
the smartphone microphone recordings. By being able to
reproduce the conditions of seemingly flawed recordings
(eg, recordings with almost no noise), informed decisions
can be made post hoc as to whether a participant should
be withdrawn from the study on grounds of problematic
recording conditions. However, no such conditions could
be identified in systematic field tests prior to the study.
Limitations
The study constitutes an extensive invasion of a partici-
pant’s privacy and implicitly requires participants to be
at least partially open to the use of consumer technology
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Open access
for health related matters. Thus, a self-selection bias42 of
participants can be expected. However, it does not seem
plausible to assume that attitudes towards study partici-
pation are associated with any aspect of a participant’s
asthma in a significant way. Therefore, the self-selection
bias appears to be a negligible limitation for the validity
of the study. However, the inclusion criterion of self-re-
ported ability to use a smartphone might introduce a
selection bias affecting the study’s generalisability, espe-
cially due to poorer technology adoption in the elderly.43
A more severe limitation is due to the fact that symptom
severity in asthma varies over time and is often driven by
seasonal changes.2 With a duration of 29 days, the proposed
study does not allow for the investigation of long-term
within-person symptom trends and patterns. The limited
study duration also prohibits the prediction of relatively
rare, but clinically more meaningful outcomes such as
asthma attacks,36 which occur on average approximately
once every 2 years per patient.44 For example, a study with
the objective to predict the risk of an asthma attack in the
next 30 days considered a period of 18 months45 in order
to obtain sufficient observations for statistical prediction.
Another important limitation of the study stems from
the conceptualisation of asthma as a disease. As a recent
expert commission has argued, asthma is not a homog-
enous, clearly defined disease but instead an umbrella
term for chronic lung disorders with distinct aetiologies,
endotypes and phenotypes.36 In this study, self-reported
physician-diagnosed asthma was defined as the main
inclusion criterion. Due to this broad and unspecific
inclusion criterion, the study sample will likely include
heterogeneous types of asthma. It is conceivable that the
predictive power of nocturnal cough and sleep quality for
asthma control may vary significantly between different
types of asthma and therefore between participants.
This issue can be addressed statistically by modelling the
predictive power of nocturnal cough and sleep quality for
asthma control as random effects. However, a statistical
solution to a conceptual issue is not a satisfying remedy
for clinical practice: if the predictive power varies consid-
erably between individual participants, health profes-
sionals would not be able to make an informed decision
at the point of care whether disease monitoring based on
nocturnal cough and sleep quality would yield a clinical
benefit for any given patient. To address this limitation,
subsequent studies could focus on specific subgroups46 to
deliver clinically useful predictions: if subgroups can be
identified for which a robust and consistent prediction
applies, nocturnal cough and sleep quality could serve as
valid digital biomarkers in certain subpopulations despite
the heterogeneity of asthma. However, if the objective of
subsequent studies is not to enable applicability but to
ensure generalisability of results, larger sample sizes will
be required to encompass asthma’s heterogeneity. From
a technological point of view, the generalisability of the
machine learning models is constrained due to the fact
that only one smartphone model is used in this study.
It is unclear to which extent the accuracy of automated
nocturnal cough detection and sleep quality assessment
would vary between smartphones models which, for
example, differ in terms of built-in sensors,47 recording
quality and number of microphones.48 Additionally,
other important factors, such as battery usage49 and
technological dependence on the smartphone’s oper-
ating system and manufacturer,50 51 need to be addressed
before the smartphone-based symptom detection models
could be implemented in clinical practice or in patient
self-monitoring.
CONCLUSION
The proposed longitudinal study investigates the preva-
lence of nocturnal cough in asthma and its potential to
predict asthma control individually and in combination
with sleep quality. Although the study design prioritises
external over internal validity, a selection bias due to poor
technology adoption in seniors and the limited sample
size might undermine generalisability. By collecting the
data with standard smartphones, the study aims to lay the
groundwork for scalable digital biomarkers for asthma.
Author afliations
1Institute of Technology Management, University of St. Gallen, St. Gallen,
Switzerland
2Lung Center, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
3Department of Management, Technology and Economics, ETH Zurich, Zurich,
Switzerland
4Institute of Epidemiology, Biostatistics and Prevention, University of Zurich, Zurich,
Switzerland
5mediX Group Practice, Zurich, Switzerland
Acknowledgements The authors would like to thank Grace Xiao for proofreading
the manuscript.
Contributors Study design: all authors. Study execution in the study centres: FR
and CS-S. Technological support of the study execution: PT and FB. Development of
study app: FB, PT and TK. Writing and editing of manuscript: PT. Critical review and
revision of manuscript: MAP, TK, FB, CS-S, FR, MB and EF. Guarantor: FR. All authors
reviewed and approved the manuscript before submission.
Funding This study is funded by CSS Insurance, Switzerland. The CSS insurance
supported the recruitment of participants but had no role in study design, app
design, data management plans, or in reviewing and approving the manuscript for
publication.
Competing interests PT, FB, EF and TK are afliated with the Center for Digital
Health Interventions ( www. c4dhi. org), a joint initiative of the Department of
Management, Technology and Economics at ETH Zurich and the Institute of
Technology Management at the University of St. Gallen, which is funded in part
by the Swiss health insurer CSS. EF and TK are also cofounders of Pathmate
Technologies, a university spin-off company that creates and delivers digital clinical
pathways and has used the open source MobileCoach platform for that purpose,
too. However, Pathmate Technologies is not involved in the study app described in
this paper.
Patient consent for publication Not required.
Ethics approval The study protocol was reviewed and approved by the ethic
commission responsible for research involving humans in eastern Switzerland
(ie, Ethikkommission Ostschweiz; Business Administration System for Ethics
Committees ID: 2017–01872).
Provenance and peer review Not commissioned; externally peer reviewed.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non-commercially,
and license their derivative works on different terms, provided the original work is
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10 TinschertP, etal. BMJ Open 2019;9:e026323. doi:10.1136/bmjopen-2018-026323
Open access
properly cited, appropriate credit is given, any changes made indicated, and the use
is non-commercial. See: http:// creativecommons. org/ licenses/ by- nc/ 4. 0/.
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... Bewegung, Ernährung, Umgang mit Stress, Tabak-und Alkoholkonsum) sowie eine Prävention psychischer Erkrankungen und Sucht erzielen, sind nicht nur psychologische Verhaltensänderungstechniken zu berücksichtigen (z.B. Zielplanung, Verhaltensbeobachtung), sondern auch Techniken zur Steigerung der Nutzung einer mHealth Applikation, z.B. der Einsatz spielerischer Elemente (Shih, Tomita et al. 2019, Tinschert, Rassouli et al. 2019. Wird eine mHealth Applikation nicht wie beabsichtigt genutzt, können die implementierten Verhaltensänderungstechniken ihre Wirkung nicht entfalten . ...
... So stellt z.B. die Integration eines interaktiven Tutorials eine Technik zur Nutzungssteigerung dar (Karekla, Kasinopoulos et al. 2019). Zudem haben sich App-, SMS-oder E-Mail-Benachrichtigungen als effektive Massnahme zur Förderung der beabsichtigten Nutzung von mHealth Applikationen erwiesen (Rodrigues, Shet et al. 2012, Alkhaldi, Modrow et al. 2017, Blakey, Bender et al. 2018, Morrissey, Casey et al. 2018, Treskes, Van der Velde et al. 2018, Künzler, Mishra et al. 2019, Musiimenta, Tumuhimbise et al. 2019, Tinschert, Rassouli et al. 2019, Achilles, Anderson et al. 2020, Goradia, Holland et al. 2020, Kramer, Künzler et al. 2020, Wiecek, Torres-Robles et al. 2020 (2) gesundheitsbezogener Verhaltensänderungen separat, jeweils über ein systematisches Literatur-Review der vorhandenen internationalen Literatur eruiert. ...
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Hintergrund: mHealth Applikationen eröffnen vielfältige Möglichkeiten zur individualisierten Prävention, zur Förderung protektiver Verhaltensweisen und des Selbstmanagements von nichtübertragbaren Krankheiten. Gleichzeitig ist deren Entwicklung und Instandhaltung im Vergleich zu browserbasierten eHealth-Anwendungen deutlich aufwändiger und Nutzenden fällt die Auswahl geeigneter Apps oft schwer. Während es zu allgemeine Qualitätskriterien wie Datenschutz, Design, Usability oder Sicherheit bereits Evaluations-Frameworks gibt wurden die notwendigen Bedingungen zur Erreichung einer Verhaltensänderung durch mHealth-Applikationen bei den Nutzenden bislang nicht systematisch recherchiert und zusammengefasst. Innerhalb von zwei separaten Literaturstudien wurden im Rahmen vorliegender Arbeit (1) Techniken zur Nutzungssteigerung und (2) Verhaltensänderungstechniken untersucht und identifiziert, die bei der Planung und Entwicklung von mHealth Applikationen berücksichtigt werden sollten und auf deren Grundlage auch die Entwicklung eines Kriterienkatalogs zur Bewertung von Gesundheits-Apps für die Nutzenden möglich ist. Fragestellungen: Im Rahmen der ersten Teilstudie wurde untersucht, welche Techniken, die in mHealth Applikationen zu NCDs, psychischer Gesundheit und Sucht eingesetzt werden, die Nutzungsadhärenz beeinflussen. Die zweite Teilstudie untersuchte, welchen Effekt Verhaltensänderungstechniken auf die intendierten Verhaltensänderungen haben. Methodik: In Teilstudie 1 wurde zur Identifikation relevanter Techniken zur Nutzungssteigerung eine systematische Literaturübersicht existierender Primärstudien erstellt. Dabei wurden in einem ersten Schritt Techniken identifiziert, die innerhalb der Primärstudien eine Verbesserung der Nutzungsadhärenz bewirkt haben. In einem zweiten Schritt wurde der Einfluss weiterer Faktoren auf die Nutzungsadhärenz untersucht, wie z.B. die Charakteristika der Zielpopulation oder die Art der Bereitstellung der Applikation. In einem dritten Schritt wurde für jede Primärstudie die Nutzungsadhärenz als Quotient beabsichtigter und tatsächlicher Nutzung berechnet, um einen Referenzwert innerhalb der verschiedenen Gesundheitsbereiche zu erhalten und mHealth Applikationen mit hoher Nutzungsadhärenz zu identifizieren. In Teilstudie 2, zur Identifikation relevanter Verhaltensänderungstechniken, wurde eine systematische Übersicht (englisch: Overview oder Umbrella Review) bereits vorhandener systematischer Reviews erstellt. Relevante wissenschaftliche Artikel für beide Teilstudien wurden durch systematische Recherchen in elektronischen Literaturdatenbanken identifiziert. Die relevante Information aus den Artikeln wurde jeweils extrahiert und analysiert. Ergebnisse: Die Literatursuche zu Teilstudie 1 ergab insgesamt 2862 potentiell relevante Artikel, von denen 99 für vorliegende Übersicht relevant waren und genauer analysiert wurden. Techniken mit positivem Einfluss auf die App-Nutzung wurden für die 7 Gesundheitsbereiche separat dargestellt, wobei folgende drei Techniken als relevant für alle Gesundheitsbereiche identifiziert wurden: (1) Personalierung bzw. die inhaltliche Anpassung der mHealth App an die individuellen Bedürfnisse der Nutzenden, (2) Erinnerungen in Form individualisierter Push-Notifikationen, (3) ein benutzerfreundliches App-Design und technische Stabilität. 5 Die aus den Primärstudien abgeleitete Nutzungsadhärenz lag durchschnittlich bei 56.0% und war am höchsten bei Lifestyle-Interventionen, welche auf die gleichzeitige Veränderung mehrerer Verhaltensweisen abzielen (60.1%) und am niedrigsten bei mHealth Apps zur Reduktion des Substanzkonsums (46.1%). Weiter ergab die quantitative Analyse eine positive Korrelation zwischen Nutzungsadhärenz und dem Grad der persönlichen Betreuung während der Intervention. Für den Bereich NCD-Selbstmanagement ergab sich eine signifikante positive Korrelation zwischen Nutzungsadhärenz und dem Durchschnittsalter der Studienteilnehmenden. Die Literatursuche zu Teilstudie 2 ergab insgesamt 615 potentiell relevante Artikel, von denen 66 für vorliegende Übersicht relevant waren und genauer analysiert wurden. Für den Bereich NCD-Selbstmanagement ist die Wirksamkeit ausschliesslich App-basierter Programme überwiegend gemischt oder noch unklar, mit der Ausnahme von Apps zum Diabetesmanagement. Zentrale Verhaltensänderungstechniken beim NCD-Selbstmanagement sind möglichst individualisierbare Zielsetzungen hinsichtlich der angestrebten Verhaltensweise (z.B. Einnahme von Medikamenten), die Selbstbeobachtung des Verhaltens (z.B. via Tagebuchfunktion in der App) und Rückmeldungen zum Verhalten (z.B. grafische Darstellung hinsichtlich dem Erreichen oder Nichterreichen des Verhaltensziels). Die Begleitung durch eine reale Fachperson scheint eine wichtige Komponente wirksamer digitaler Programme zur Unterstützung des Umgangs mit chronischen Erkrankungen. Auch die Evidenz zur Wirksamkeit App-basierter Programme zur Änderung des Ernährungsverhaltens ist noch gemischt, wobei eine Ernährungsumstellung, z.B. durch die Steigerung des Obst- und Gemüsekonsums häufiger erreicht werden kann als eine Reduktion der aufgenommenen Energiemenge. Die bislang eingesetzten mHealth Applikationen nutzen überwiegend Verhaltensänderungstechniken, die sich auch in traditionellen Einzel- und Gruppenberatungen zur Veränderung des Ernährungsverhaltens bewährt haben: Individuelle Zielsetzungen, Verhaltensbeobachtung und –rückmeldung sowie soziale Unterstützung. Inwieweit andere Techniken, wie z.B. die Veränderung des Selbstbilds oder soziale Vergleiche wirksam sind, lässt sich auf Grundlage der bisherigen Daten nicht beantworten. Die Wirksamkeit von Apps zur Steigerung körperlicher Aktivität ist mittlerweile wissenschaftlich gut fundiert, wobei insbesondere kranke und gefährdete Bevölkerungsgruppen von diesen profitieren. Auch hier spielen die Festlegung individueller Aktivitätsziele, deren Beobachtung und Feedbacks zu deren Erreichung eine zentrale Rolle. Die Einbeziehung einer realen Fachperson scheint bei diesen Programmen nicht notwendig. Dagegen sind Programme effektiver, welche vom System (z.B. via Bewegungssensor) automatisiert erfasste Daten für die Individualisierung verwenden. Bei Apps zur Gewichtsreduktion und zur gleichzeitigen Veränderung mehrerer Verhaltensweisen (sog. Lifestyle-Interventionen), die meist durch Förderung körperlicher Aktivität und gesunder Ernährung auch auf Gewichtsreduktion zielen, ist die Wirksamkeit gemischt. Zentrale Komponenten sind die Verwendung mehrerer und interaktiver Verhaltensänderungstechniken, insbesondere zur Zielsetzung sowie Verhaltensbeobachtung und Feedback. Bei Programmen zur Verbesserung der psychischen Gesundheit haben sich Elemente der kognitiven Verhaltenstherapie bewährt, um via Internet oder App Angst und Depressivität zu reduzieren. Ähnlich den Selbstmanagement-Programmen bei NCDs scheint auch hier die persönliche Begleitung durch eine Fachperson der Wirksamkeit dienlich. Neben der Selbstbeobachtung des Verhaltens stellen die Veränderung kognitiver Prozesse (z.B. Steigerung positiver Gedanken, kognitiver Flexibilität, wahrgenommener Kontrolle) und von Fähigkeiten (z.B. Anwendung von Mindfulness Skills oder kognitiv-behavioraler Techniken) zentrale Wirkmechanismen dar. Die Evidenz zur Wirksamkeit von App-Programmen zur Reduktion des Alkoholkonsums in der Allgemeinbevölkerung ist bislang gemischt, mit einzelnen positiven aber auch vielen Studien ohne signifikante Ergebnisse. Erfolgreiche Programme zeichnen sich insbesondere dadurch aus, dass sie Nutzenden praktische, leicht umsetzbare Hinweise zum Ersetzen des Alkoholkonsums und zur Problemlösung anbieten; diese sollten von einer als glaubwürdig wahrgenommenen Quelle kommen. Auch die Evidenz zur Wirksamkeit von Apps zur Entwöhnung vom Tabakrauchen ist bislang recht heterogen. In Reviews zu primär Internetbasierten Programmen waren verschiedene Techniken mit der Wirksamkeit assoziiert: das Setzen konkreter Verhaltensziele und Handlungsplanung, Hinweise zur Problemlösung und zu gesundheitlichen Folgen des Rauchens, die Abwägung von Vor- und Nachteilen des Rauchstopps aber auch soziale und medikamentöse Unterstützung. Schlussfolgerungen und Empfehlungen: Zentral für eine hohe App-Nutzung und Wirksamkeit sind Technologien zur Personalisierung und Individualisierung der Inhalte. Persönlich relevante Verhaltensziele sollten durch die Nutzenden festgelegt und deren Grad der Realisierung über die Zeit hinweg durch die App beobachtet werden können. Insbesondere geeignet sind dabei interaktive Funktionen, welche neben dem Grad der Zielerreichung auch Charakteristika der Person und des Kontextes berücksichtigen. Regelmässige Erinnerungen durch die App, welche die individuelle Verfügbarkeit und das Bedürfnis nach Interaktion berücksichtigen, stellen eine wesentliche Voraussetzung dar, um diese zentralen Techniken zur Zielsetzung, Verhaltensbeobachtung und –rückmeldung über einen längeren Zeitraum einzusetzen. Neben diesen automatisierten Funktionen bilden Möglichkeiten zur persönlichen Begleitung und sozialen Unterstützung, insbesondere bei Apps die in klinischen Gruppen eingesetzt werden, ein wesentliches Element für deren Nutzung und Wirksamkeit. Für die regelmässige Nutzung sind ausserdem technische Stabilität sowie ein benutzerfreundliches App-Design relevant. Insgesamt ist die Forschung zu erfolgversprechenden Techniken zur Nutzungssteigerung sowie zu Verhaltensänderungstechniken bei mHealth Apps noch wenig fortgeschritten. Die zugrundeliegenden Studien haben häufig Pilotcharakter, die Umsetzung der Techniken und Operationalisierung der Ergebnisse ist sehr uneinheitlich. Da mHealth Apps meist mehrere Techniken zur Nutzungssteigerung und Verhaltensänderung verwenden, sind kausale Aussagen über einzelne Techniken kaum möglich. Dazu sind zukünftig vermehrt kontrollierte und experimentelle Studien notwendig. Die empfohlenen Techniken zur individualisierten Zielsetzung, Verhaltensbeobachtung, Rückmeldung, Erinnerung und sozialen Unterstützung stellen auch Grundelemente aktueller Modelle zum Gesundheitsverhalten und bewährter kognitiv-verhaltenstherapeutischer Interventionen dar. Deren Integration in mHealth Applikationen bildet ein solides Fundament. Für deren Optimierung sollten zukünftig aber gleichzeitig auch neue Techniken erprobt und überprüft werden, deren volles Potential erst durch digitale Technologien ausgeschöpft werden kann.
... These periods marked as silence served as visual aids for the rest of the annotation process. Human annotators listened to the smartphone recordings and marked the periods not marked as silence as coughs when an explosive cough sound was identified [11,32]. ...
... The second data set is a labeled set of audio recordings consisting of nocturnal reflex coughs from asthmatic patients collected in a multicenter, longitudinal, observational study [25]. The corresponding study protocol was approved by the ethics commission responsible for research involving humans in eastern Switzerland in November, 2017 (BASEC ID: 2017-01872). ...
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Cough, a symptom associated with many prevalent respiratory diseases, can serve as a potential biomarker for diagnosis and disease progression. Consequently, the development of cough monitoring systems and, in particular, automatic cough detection algorithms have been studied since the early 2000s. Recently, there has been an increased focus on the efficiency of such algorithms, as implementation on consumer-centric devices such as smartphones would provide a scalable and affordable solution for monitoring cough with contact-free sensors. Current algorithms, however, are incapable of discerning between coughs of different individuals and, thus, cannot function reliably in situations where potentially multiple individuals have to be monitored in shared environments. Therefore, we propose a weakly supervised metric learning approach for cougher recognition based on smartphone audio recordings of coughs. Our approach involves a triplet network architecture, which employs convolutional neural networks (CNNs). The CNNs of the triplet network learn an embedding function, which maps Mel spectrograms of cough recordings to an embedding space where they are more easily distinguishable. Using audio recordings of nocturnal coughs from asthmatic patients captured with a smartphone, our approach achieved a mean accuracy of 88% (10% SD) on two-way identification tests with 12 enrollment samples and accuracy of 80% and an equal error rate (EER) of 20% on verification tests. Furthermore, our approach outperformed human raters with regard to verification tests on average by 8% in accuracy, 4% in false acceptance rate (FAR), and 12% in false rejection rate (FRR). Our code and models are publicly available.
... The authors analysed both spontaneous and voluntary coughs and only required five cough samples per patient which could be collected at the time of the patient's hospital visit rather than having to take a cough monitor home with the patient. Overnight coughing has been identified as a potential biomarker for asthma control [27].We argue that exclusive overnight monitoring of cough frequency would not be appropriate for patients with chronic cough due to the low frequency of nocturnal cough. ...
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Research question Objective quantification of cough is rarely utilised outside of research settings and the role of cough frequency monitoring in clinical practice has not been established. This study examined the clinical utility of cough frequency monitoring in an outpatient clinical setting. Methods The study involved a retrospective review of cough monitor data. Participants included 174 patients referred for treatment of cough and upper airway symptoms (103 chronic cough; 50 inducible laryngeal obstruction; 21 severe asthma) and 15 controls. Measures, taken prior to treatment, included 24-h ambulatory cough frequency using the Leicester Cough Monitor, the Leicester Cough Questionnaire and Laryngeal Hypersensitivity Questionnaire. Post-treatment data were available for 50 participants. Feasibility and clinical utility were also reported. Results Analysis time per recording was up to 10 min. 75% of participants could use the monitors correctly, and most (93%) recordings were interpretable. The geometric mean cough frequency in patients was 10.1±2.9 (mean± sd ) compared to 2.4±2.0 for healthy controls (p=0.003). There was no significant difference in cough frequency between clinical groups (p=0.080). Cough frequency decreased significantly following treatment (p<0.001). There was a moderate correlation between cough frequency and both cough quality of life and laryngeal hypersensitivity. Cough frequency monitoring was responsive to therapy and able to discriminate differences in cough frequency between diseases. Conclusion While ambulatory cough frequency monitoring remains a research tool, it provides useful clinical data that can assist in patient management. Logistical issues may preclude use in some clinical settings, and additional time needs to be allocated to the process.
... The Benefit StepCoach app was implemented with MobileCoach (www.mobile-coach.eu) [52,53,54], an open-source software platform for smartphone-based and chatbot-delivered behavioral interventions (eg, [55]) and ecological momentary assessments (eg, [56] Appropriate interactions were implemented, i.e. asking participants for their permission, to allow the apps to access the step data. Moreover, each experimental group was assigned a dedicated TCA. ...
Preprint
BACKGROUND Self-help eHealth interventions are generally less effective than human-supported ones, as they suffer from a low level of adherence. Nevertheless, self-help interventions are useful in the prevention of non-communicable diseases, as they are easier and cheaper to widely implement. Adding humanness in the form of a text-based conversational agent (TCA) could provide a solution to non-adherence. In this study we investigate whether adding human cues to a TCA facilitates relationship-building with the agent, and makes interventions more attractive for people to adhere to. We will investigate the effects of two types of human cues, which are visual cues (eg, human avatar) and relational cues (eg, showing empathy). OBJECTIVE We aim to investigate if adding human cues to a TCA can help increase adherence to a self-help eHealth lifestyle intervention and explore the role of working alliance as a possible mediator of this relationship. METHODS Participants (N=121) followed a 3-week app-based physical activity intervention delivered by a TCA. Two types of human cues used by the TCA were manipulated, resulting in four experimental groups, which were (1) visual cues-group, (2) relational cues-group, (3) both visual and relational cues-group, and (4) no cues-group. Participants filled out the Working Alliance Inventory Short Revised form after the final day of the intervention. Adherence was measured as number of days participants responded to the messages of the TCA. RESULTS One-way ANOVA revealed a significant difference for adherence between conditions. Against our expectations, the groups with visual cues showed lower adherence compared to those with relational only or no cues (t(117) = -3.415, P = .001). No significant difference was found between the relational- and no cues-groups. Working alliance was not affected by cue-type, but showed to have a significant positive relationship with adherence (t(75) = 4.136, P < .001). CONCLUSIONS We hypothesize that the negative effect of visual cues is due to a lack of transparency about the true nature of the coach. Visual resemblance of a human coach could have led to high expectations that could not be met by our digital coach. Furthermore, the inability of TCAs to use non-verbal communication could provide an explanation for the lack of effect of relational cues or the effect of cue-type on working alliance. We give suggestions for future studies to test these potential mechanisms. CLINICALTRIAL Pre-registration: OSF Registries, https://osf.io/mgw2s
... The study protocol of this two-center, longitudinal, observational study has already been published 14 (Clinicaltrials No: NCT03635710). Here, we report results on the association of nocturnal cough, sleep quality, asthma control, and asthma attacks (2nd study stage). ...
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Introduction: Objective markers for asthma, that can be measured without extra patient effort, could mitigate current shortcomings in asthma monitoring. We investigated whether smartphone-recorded nocturnal cough and sleep quality can be utilized for the detection of periods with uncontrolled asthma or meaningful changes in asthma control and for the prediction of asthma attacks. Methods: We analyzed questionnaire and sensor data of 79 adults with asthma. Data were collected in situ for 29 days by means of a smartphone. Sleep quality and nocturnal cough frequencies were measured every night with the Pittsburgh Sleep Quality Index and by manually annotating coughs from smartphone audio recordings. Primary endpoint was asthma control assessed with a weekly version of the Asthma Control Test. Secondary endpoint was self-reported asthma attacks. Results: Mixed-effects regression analyses showed that nocturnal cough and sleep quality were statistically significantly associated with asthma control on a between- and within-patient level (p < 0.05). Decision trees indicated that sleep quality was more useful for detecting weeks with uncontrolled asthma (balanced accuracy (BAC) 68% vs 61%; Δ sensitivity -12%; Δ specificity -2%), while nocturnal cough better detected weeks with asthma control deteriorations (BAC 71% vs 56%; Δ sensitivity 3%; Δ specificity -34%). Cut-offs using both markers predicted asthma attacks up to five days ahead with BACs between 70% and 75% (sensitivities 75 - 88% and specificities 57 - 72%). Conclusion: Nocturnal cough and sleep quality have useful properties as markers for asthma control and seem to have prognostic value for the early detection of asthma attacks. Due to the limited study duration per patient and the pragmatic nature of the study, future research is needed to comprehensively evaluate and externally validate the performance of both biomarkers and their utility for asthma self-management.
... The study protocol of this two-center, longitudinal, observational study has already been published 14 (Clinicaltrials No: NCT03635710). Here, we report results on the association of nocturnal cough, sleep quality, asthma control, and asthma attacks (2nd study stage). ...
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Each year, more than 800 000 persons die by suicide, making it a leading cause of death worldwide. Recent innovations in information and communication technology may offer new opportunities in suicide prevention in individuals, hereby potentially reducing this number. In our project, we design digital indices based on both self-reports and passive mobile sensing and test their ability to predict suicidal ideation, a major predictor for suicide, and psychiatric hospital readmission in high-risk individuals: psychiatric patients after discharge who were admitted in the context of suicidal ideation or a suicidal attempt, or expressed suicidal ideations during their intake. Specifically, two smartphone applications -one for self-reports (SIMON-SELF) and one for passive mobile sensing (SIMON-SENSE)- are installed on participants’ smartphones. SIMON-SELF uses a text-based chatbot, called Simon, to guide participants along the study protocol and to ask participants questions about suicidal ideation and relevant other psychological variables five times a day. These self-report data are collected for four consecutive weeks after study participants are discharged from the hospital. SIMON-SENSE collects behavioral variables -such as physical activity, location, and social connectedness- parallel to the first application. We aim to include 100 patients over 12 months to test whether (1) implementation of the digital protocol in such a high-risk population is feasible, and (2) if suicidal ideation and psychiatric hospital readmission can be predicted using a combination of psychological indices and passive sensor information. To this end, a predictive algorithm for suicidal ideation and psychiatric hospital readmission using various learning algorithms (e.g. random forest and support vector machines) and multilevel models will be constructed. Data collected on the basis of psychological theory and digital phenotyping may, in the future and based on our results, help reach vulnerable individuals early and provide links to just-in-time and cost-effective interventions or establish prompt mental health service contact. The current effort may thus lead to saving lives and significantly reduce economic impact by decreasing inpatient treatment and days lost to inability.
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Background: The current coronavirus disease 2019 (COVID-19) pandemic has caused a significant strain on medical resources throughout the world. A major shift to telemedicine and mobile health technologies has now taken on an immediate urgency. Newly developed devices designed for home use have facilitated remote monitoring of various physiologic parameters relevant to pulmonary diseases. These devices have also enabled home-based pulmonary rehabilitation programs. In addition, telemedicine and home care services have been leveraged to rapidly develop acute care hospital-at-home programs for the treatment of mild-to-moderate COVID-19 illness. Areas of uncertainty: The benefit of remote monitoring technologies on patient outcomes has not been established in robust trials. Furthermore, the use of these devices, which can increase the burden of care, has not been integrated into current clinical workflows and electronic medical records. Finally, reimbursement for these telemedicine and remote monitoring services is variable. Data sources: Literature review. Therapeutic advances: Advances in digital technology have improved remote monitoring of physiologic parameters relevant to pulmonary medicine. In addition, telemedicine services for the provision of pulmonary rehabilitation and novel hospital-at-home programs have been developed. These new home-based programs have been adapted for COVID-19 and may also be relevant for the management of acute and chronic pulmonary diseases after the pandemic. Conclusion: Digital remote monitoring of physiologic parameters relevant to pulmonary medicine and novel hospital-at-home programs are feasible and may improve care for patients with acute and chronic respiratory-related disorders.
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Working alliance describes an important relationship quality between health professionals and patients and is robustly linked to treatment success. However, due to limited resources of health professionals, working alliance cannot always be promoted just-in-time in a ubiquitous fashion. To address this scalability problem, we investigate the direct effect of interpersonal closeness cues of text-based healthcare chatbots (THCBs) on attachment bond from the working alliance con-struct and the indirect effect on the desire to continue interacting with THCBs. The underlying research model and hypotheses are informed by counselling psychology and research on conver-sational agents. In order to investigate the hypothesized effects, we first develop a THCB codebook with 12 design dimensions on interpersonal closeness cues that are categorized into visual cues (i.e. avatar), verbal cues (i.e. greetings, address, jargon, T-V-distinction), quasi-nonverbal cues (i.e. emoticons) and relational cues (i.e. small talk, self-disclosure, empathy, humor, meta-relational talk and continuity). In a second step, four distinct THCB designs are developed along the continuum of interpersonal closeness (i.e. institutional-like, expert-like, peer-like and myself-like THCBs) and a corresponding study design for an interactive THCB-based online experiment is presented to test our hypotheses. We conclude this work-in-progress by outlining our future work.
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Introduction Cough intensity is an important determinant of cough severity reported by patients. Cough sound analysis has been widely validated for the measurement of cough frequency but few studies have validated its use in the assessment of cough strength. We investigated the relationship between cough sound and physiological measures of cough strength. Methods 32 patients with chronic cough and controls underwent contemporaneous measurements of voluntary cough sound, flow and oesophageal pressure. Sound power, peak energy, rise-time, duration, peak-frequency, bandwidth and centroid-frequency were assessed and compared with physiological measures. The relationship between sound and subjective cough strength Visual Analogue Score (VAS), the repeatability of cough sounds and the effect of microphone position were also assessed. Results Sound power and energy correlated strongly with cough flow (median Spearman’s r=0.87–0.88) and oesophageal pressure (median Spearman’s r=0.89). Sound power and energy correlated strongly with cough strength VAS (median Spearman’s r=0.84–0.86) and were highly repeatable (intraclass correlation coefficient=0.93–0.94) but both were affected by change in microphone position. Conclusions Cough sound power and energy correlate strongly with physiological measures and subjective perception of cough strength. Power and energy are highly repeatable measures but the microphone position should be standardised. Our findings support the use of cough sound as an index of cough strength.
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Background: Asthma exacerbations are frequent in patients with severe disease. This report describes results from two retrospective cohort studies describing exacerbation frequency and risk, emergency department (ED)/hospital re-admissions, and asthma-related costs by asthma severity in the US and UK. Methods: Patients with asthma in the US-based Clinformatics™ DataMart Multiplan IMPACT (2010-2011; WEUSKOP7048) and the UK-based Clinical Practice Research Datalink (2009-2011; WEUSKOP7092) databases were categorized by disease severity (Global Initiative for Asthma [GINA]; Step and exacerbation history) during the 12 months pre-asthma medical code (index date). Outcomes included: frequency of exacerbations (asthma-related ED visit, hospitalization, or oral corticosteroid use with an asthma medical code recorded within ±2 weeks) 12 months post-index, asthma-related ED visits/hospitalization, and asthma-related costs 30 days post-index. Risk of a subsequent exacerbation was determined by proportional hazard model. Results: Of the 222,817 and 211,807 patients with asthma included from the US and UK databases, respectively, 12.5 and 8.4% experienced ≥1 exacerbation during the follow-up period. Exacerbation frequency increased with disease severity. Among the 5,167 and 2,904 patients with an asthma-related ED visit/hospitalization in the US and UK databases, respectively, 9.2 and 4.7% had asthma-related re-admissions within 30 days. Asthma-related re-admission rates and costs increased with disease severity, approximately doubling between GINA Step 1 and 5 and in patients with ≥2 versus <2 exacerbations in the previous year. Risk of a subsequent exacerbation increased 32-35% for an exacerbation requiring ED visit/hospitalization versus oral corticosteroids. Conclusion: Increased disease severity was associated with higher exacerbation frequency, ED/hospitalization re-admission, costs and risk of subsequent exacerbation, indicating that these patients require high-intensity post-exacerbation management.
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Purpose: A large number of asthmatic patients, particularly females, present inadequate disease control. Depressive symptoms are reportedly common in asthma and have been related to poor disease control, but the mechanism of this association is still unclear. Poor quality sleep, frequently observed in asthmatics, is also a manifestation of depression and has been related to uncontrolled asthma. This study aimed to investigate the relationship between depressive symptoms, sleep quality, and asthma control. Methods: This was a cross-sectional study of 123 women with previous diagnosis of asthma from a reference center in Fortaleza, Brazil. Depressive symptoms were assessed by the Beck Depression Inventory (BDI); quality of sleep was evaluated by the Pittsburgh Sleep Quality Index (PSQI), daytime sleepiness by the Epworth Sleepiness Scale (ESS), and asthma control by the Asthma Control Test (ACT). Results: Inadequate asthma control (ACT <20) was found in 94 (76.4 %) subjects, depressive symptoms in 92 (74.8 %), poor quality sleep (PSQI >5) in 99 (80.49 %), and excessive daytime sleepiness (ESS ≥10) in 34 (27.64 %). Depressive symptoms were associated with both poor quality sleep (R = 0.326) and inadequate asthma control (R = -0.299). Regression analysis showed that depressive symptoms and sleep quality were independent predictors of the level of asthma control. Conclusion: Asthma control in women is independently associated with depressive symptoms and quality of sleep, suggesting that these patients might benefit from simple measures to promote healthy sleep behavior and sleep hygiene and also that routine screening for depression can be relevant, particularly, in poorly controlled cases.
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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.
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The influence of the circadian clock on sleep scheduling has been studied extensively in the laboratory; however, the effects of society on sleep remain largely unquantified. We show how a smartphone app that we have developed, ENTRAIN, accurately collects data on sleep habits around the world. Through mathematical modeling and statistics, we find that social pressures weaken and/or conceal biological drives in the evening, leading individuals to delay their bedtime and shorten their sleep. A country's average bedtime, but not average wake time, predicts sleep duration. We further show that mathematical models based on controlled laboratory experiments predict qualitative trends in sunrise, sunset, and light level; however, these effects are attenuated in the real world around bedtime. Additionally, we find that women schedule more sleep than men and that users reporting that they are typically exposed to outdoor light go to sleep earlier and sleep more than those reporting indoor light. Finally, we find that age is the primary determinant of sleep timing, and that age plays an important role in the variability of population-level sleep habits. This work better defines and personalizes "normal" sleep, produces hypotheses for future testing in the laboratory, and suggests important ways to counteract the global sleep crisis.
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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.
Aims: A longitudinal birth cohort provides an opportunity to study the impact of childhood conditions persisting into adulthood. This study examines the cross-sectional association of asthma with sleep quality and snoring in the adolescent and young adult population and the extent to which asthma, sleep quality and snoring at 14 years independently predict themselves or each other at 21 years. Methods: Data from a 21-year follow-up of mothers and their children recruited into the Mater-University of Queensland Study of Pregnancy (n = 7223). Complete asthma and sleep information (questionnaire data) was available for 5015 participants at 14 years and 3527 at 21 years, with 3237 participants at both 14 and 21 years. Results: Poor sleep quality and snoring were independently associated with asthma at 14 years and 21 years, with stronger associations evident in women. At 21 years, associations were mediated by asthma symptom severity. Asthma, sleep quality and snoring at 14 years each strongly and independently predicted themselves at 21 years. Asthma at 14 years predicted snoring at 21 years, while poor sleep quality and snoring in women predicted asthma at 21 years, the latter partially mediated by body mass index. Conclusions: The relationship between asthma, sleep quality and snoring varied by gender. Sleep quality and snoring should be considered in the assessment and holistic management of asthma. The predictive relationship seen between 14 and 21 years provides an opportunity to address these issues at a younger age.