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

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 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.

Full text

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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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TinschertP, etal. BMJ Open 2019;9:e026323. doi:10.1136/bmjopen-2018-026323
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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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