Now showing 1 - 10 of 20
  • Publication
    Automatic Recognition, Segmentation, and Sex Assignment of Nocturnal Asthmatic Coughs and Cough Epochs in Smartphone Audio Recordings: Observational Field Study
    (JMIR Publications Inc., 2020-07)
    Barata, Filipe
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    Rassouli, Frank
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    Steurer-Stey, Claudia
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    Puhan, Milo Alan
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    Brutsche, Martin
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    Kotz, David
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    Background: Asthma is one of the most prevalent chronic respiratory diseases. Despite increased investment in treatment, little progress has been made in the early recognition and treatment of asthma exacerbations over the last decade. Nocturnal cough monitoring may provide an opportunity to identify patients at risk for imminent exacerbations. Recently developed approaches enable smartphone-based cough monitoring. These approaches, however, have not undergone longitudinal overnight testing nor have they been specifically evaluated in the context of asthma. Also, the problem of distinguishing partner coughs from patient coughs when two or more people are sleeping in the same room using contact-free audio recordings remains unsolved. Objective: The objective of this study was to evaluate the automatic recognition and segmentation of nocturnal asthmatic coughs and cough epochs in smartphone-based audio recordings that were collected in the field. We also aimed to distinguish partner coughs from patient coughs in contact-free audio recordings by classifying coughs based on sex. Methods: We used a convolutional neural network model that we had developed in previous work for automated cough recognition. We further used techniques (such as ensemble learning, minibatch balancing, and thresholding) to address the imbalance in the data set. We evaluated the classifier in a classification task and a segmentation task. The cough-recognition classifier served as the basis for the cough-segmentation classifier from continuous audio recordings. We compared automated cough and cough-epoch counts to human-annotated cough and cough-epoch counts. We employed Gaussian mixture models to build a classifier for cough and cough-epoch signals based on sex. Results: We recorded audio data from 94 adults with asthma (overall: mean 43 years; SD 16 years; female: 54/94, 57%; male 40/94, 43%). Audio data were recorded by each participant in their everyday environment using a smartphone placed next to their bed; recordings were made over a period of 28 nights. Out of 704,697 sounds, we identified 30,304 sounds as coughs. A total of 26,166 coughs occurred without a 2-second pause between coughs, yielding 8238 cough epochs. The ensemble classifier performed well with a Matthews correlation coefficient of 92% in a pure classification task and achieved comparable cough counts to that of human annotators in the segmentation of coughing. The count difference between automated and human-annotated coughs was a mean –0.1 (95% CI –12.11, 11.91) coughs. The count difference between automated and human-annotated cough epochs was a mean 0.24 (95% CI –3.67, 4.15) cough epochs. The Gaussian mixture model cough epoch–based sex classification performed best yielding an accuracy of 83%. Conclusions: Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma. The model distinguishes partner cough from patient cough in contact-free recordings by identifying cough and cough-epoch signals that correspond to the sex of the patient. This research represents a step towards enabling passive and scalable cough monitoring for adults with asthma.
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  • Publication
    Characteristics of Asthma-related Nocturnal Cough: A Potential New Digital Biomarker
    (Dove Medical Press, 2020-12-03)
    Rassouli, Frank
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    Barata, Filipe
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    Steurer-Stey, Claudia
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    Puhan, Milo A.
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    Baty, Florent
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    Brutsche, Martin H.
    Introduction: The nature of nocturnal cough is largely unknown. It might be a valid marker for asthma control but very few studies characterized it as a basis for better defining its role and its use as clinical marker. This study investigated prevalence and characteristics of nocturnal cough in asthmatics over the course of four weeks. Methods: In two centers, 94 adult patients with physician-diagnosed asthma were recruited. Patient-reported outcomes and nocturnal sensor data were collected by a smartphone with a chat-based study app. Results: Patients coughed in 53% of 2212 nights (range: 0-345 coughs/night). Median coughs per hour were 0 (IQR 0-1). Nocturnal cough rates showed considerable inter-individual variance. The highest counts were measured in the first 30 min in bed (4.5-fold higher than rest of night). Eighty-six percent of coughs were part of a cough cluster. Clusters consisted of a median of two coughs (IQR 2-4). Nocturnal cough was persistent within patient. Conclusion: To the best of the authors' knowledge, this study is the first to describe prevalence and characteristics of nocturnal cough in asthma over a period of one month, demonstrating that it was a prevalent symptom with large variance between patients and high persistence within patients. Cough events in asthmatics were 4.5 times more frequent within the first 30 min in bed indicating a potential role of positional change, and not more frequent during the early morning hours. An important next step will investigate the association between nocturnal cough and asthma control.
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    Scopus© Citations 12
  • Publication
    Nocturnal cough and sleep quality to assess asthma control and predict attacks
    (Dove Medical Press, 2020-12-14) ;
    Rassouli, Frank
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    Barata, Filipe
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    Steurer-Stey, Claudia
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    Puhan, Milo A.
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    Brutsche, Martin H.
    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 were 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 < .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.
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    Scopus© Citations 12
  • Publication
    A Cluster-Randomized Trial on Small Incentives to Promote Physical Activity
    Introduction: There has been limited research investigating whether small financial incentives can promote participation, behaviour change, and engagement in physical activity promotion programs. This study evaluates the effects of two types of small financial incentives within a physical activity promotion program of a Swiss health insurance company. Study Design: Three-arm cluster-randomized trial comparing small personal financial incentives and charity financial incentives (10 Swiss Francs, equal to $10.4) for each month with an average step count of at least 10,000 steps per day) to control. Insurees' federal state of residence was the unit of randomization. We collected data in 2015 and completed the analyses in 2018. Setting/participants: We invited German-speaking insurees of a large health insurer in Switzerland. Invited insurees were aged ≥ 18 years, enrolled in complementary insurance plans and registered on the insurer's online platform. Main outcome measures: Primary outcome was the participation rate. Secondary outcomes were steps per day, participant days that more than 10,000 steps were achieved and non-usage attrition over the first three months of the program. Results: Participation rate was 5.94% in the personal financial incentive group (OR: 1.96; 95% CI: 1.55 to 2.49) and 4.98% in the charity financial incentive group (OR: 1.59; 95% CI: 1.25 to 2.01) compared to 3.23% in the control group. At the start of the program, the charity financial group had a 12% higher chance to walk 10,000 steps per day than the control group (OR: 1.68; 95% CI: 1.23 to 2.30), but this effect dissipated after three months. Steps per day and non-usage attrition did not differ significantly between the groups. Conclusions: Small personal and charity financial incentives can increase participation in physical activity promotion programs. Incentives may need to be modified in order to prevent attrition and promote behaviour change over a longer period of time.
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  • Publication
    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, Multi-Centre, Longitudinal Observational Study with Two Stages
    ( 2019-01-07) ;
    Rassouli, Frank
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    Barata, Filipe
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    Steurer-Stey, Claudia
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    Puhan, Milo Alan
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    Brutsche, Martin Hugo
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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 multi-centre, longitudinal observational study with two stages. Sensor and questionnaire data of 94 asthmatics 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 (FeNO) levels. Asthma control will be assessed by the Asthma Control Test (ACT) and sleep quality by means of the Pittsburgh Sleep Quality Index (PSQI). 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). It is registered at clinicaltrials.gov (NCT03635710). All study data will be anonymized upon study termination. Results will be published in medical and technical peer-reviewed journals.
    Scopus© Citations 15
  • Publication
    The Potential of Mobile Apps for Improving Asthma Self-Management: A Review of Publicly Available and Well-Adopted Asthma Apps
    ( 2017) ;
    Jakob, Robert
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    Barata, Filipe
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    Background: Effective disease self-management lowers asthma’s burden of disease for both individual patients and health care systems. In principle, mobile health (mHealth) apps could enable effective asthma self-management interventions that improve a patient’s quality of life while simultaneously reducing the overall treatment costs for health care systems. However, prior reviews in this field have found that mHealth apps for asthma lack clinical evaluation and are often not based on medical guidelines. Yet, beyond the missing evidence for clinical efficacy, little is known about the potential apps might have for improving asthma self-management. Objective: The aim of this study was to assess the potential of publicly available and well-adopted mHealth apps for improving asthma self-management. Methods: The Apple App store and Google Play store were systematically searched for asthma apps. In total, 523 apps were identified, of which 38 apps matched the selection criteria to be included in the review. Four requirements of app potential were investigated: app functions, potential to change behavior (by means of a behavior change technique taxonomy), potential to promote app use (by means of a gamification components taxonomy), and app quality (by means of the Mobile Application Rating Scale [MARS]). Results: The most commonly implemented functions in the 38 reviewed asthma apps were tracking (30/38, 79%) and information (26/38, 68%) functions, followed by assessment (20/38, 53%) and notification (18/38, 47%) functions. On average, the reviewed apps applied 7.12 of 26 available behavior change techniques (standard deviation [SD]=4.46) and 4.89 of 31 available gamification components (SD=4.21). Average app quality was acceptable (mean=3.17/5, SD=0.58), whereas subjective app quality lied between poor and acceptable (mean=2.65/5, SD=0.87). Additionally, the sum scores of all review frameworks were significantly correlated (lowest correlation: r36=.33, P=.04 between number of functions and gamification components; highest correlation: r36=.80, P<.001 between number of behavior change techniques and gamification components), which suggests that an app’s potential tends to be consistent across review frameworks. Conclusions: Several apps were identified that performed consistently well across all applied review frameworks, thus indicating the potential mHealth apps offer for improving asthma self-management. However, many apps suffer from low quality. Therefore, app reviews should be considered as a decision support tool before deciding which app to integrate into a patient’s asthma self-management. Furthermore, several research-practice gaps were identified that app developers should consider addressing in future asthma apps.
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    Scopus© Citations 112
  • Publication
    Towards Device-Agnostic Mobile Cough Detection with Convolutional Neural Networks
    (IEEE, 2019-06)
    Barata, Filipe
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    Kipfer, Kevin
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    Weber, Maurice
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    Ubiquitous mobile devices have the potential to reduce the financial burden of healthcare systems by providing scalable and cost-efficient health monitoring applications. Coughing is a symptom associated with prevalent pulmonary diseases, and bears great potential for being exploited by monitoring applications. Prior research has shown the feasibility of cough detection by smartphone-based audio recordings, but it is still open as to whether current detection models generalize well to a variety of mobile devices to ensure scalability. We first conducted a lab study with 43 subjects and recorded 6737 cough samples and 8854 control sounds by 5 different recording devices. We then reimplemented two approaches from prior work and investigated their performance in two different scenarios across devices. We propose an efficient convolutional neural network architecture and an ensemble based classifier to reduce the cross-device discrepancy. Our approach produced mean accuracies in the range [85.9%, 90.9%], showing consistency across devices (SD = [1.5%, 2.7%]) and outperforming prior learning algorithms. Thus, our proposal is a step towards cost-efficient, ubiquitous, scalable and device-agnostic cough detection.
  • Publication
    Enhancing Asthma Control through IT: Design, Implementation and Planned Evaluation of the Mobile Asthma Companion
    The personal and financial burden of asthma highly depends on a patient’s disease self-management skill. Scalable mHealth apps, designed to empower patients, have the potential to play a crucial role in asthma disease management. However, the actual clinical efficacy of mHealth asthma apps is poorly understood due to the lack of both methodologically sound research and accessible evidence-based apps. We therefore apply design science with the goal to design, implement and evaluate a mHealth app for people with asthma, the Mobile Asthma Companion (MAC). The current prototype of MAC delivers health literacy knowledge triggered by nocturnal cough rates. We conclude by proposing a randomized controlled trial to test the efficacy of our prototype.
  • Publication
    Personal MobileCoach: Tailoring Behavioral Interventions to the Needs of Individual Participants
    MobileCoach, an open source behavioral intervention platform, has been developed to provide health professionals with an authoring tool to design evidence-based, scalable and low-cost digital health interventions (DHI). Its potential meets the lack in resources and capacity of health care systems to provide DHI for the treatment of 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.
    Scopus© Citations 6
  • Publication
    Towards The Design of a Smartphone-Based Biofeedback Breathing Training: Identifying Diaphragmatic Breathing Patterns from a Smartphone’s Microphone
    (AIS Electronic Library (AISeL), 2016-09-04)
    Shih, Chen-Hsuan Iris
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    Barata, Filipe
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    Nißen, Marcia Katharina
    Asthma, diabetes, hypertension, or major depression are non-communicable diseases (NCDs) and impose a major burden on global health. Stress is linked to both the causes and consequences of NCDs and it has been shown that biofeedback-based breathing trainings (BBTs) are effective in coping with stress. Here, diaphragmatic breathing, i.e. deep abdominal breathing, belongs to the most distinguished breathing techniques. However, high costs and low scalability of state-of-the-art BBTs that require expensive medical hardware and health professionals, represent a significant barrier for their widespread adoption. Health information technology has the potential to address this important practical problem. Particularly, it has been shown that a smartphone microphone has the ability to record audio signals from exhalation in a quality that can be compared to professional respiratory devices. As this finding is highly relevant for low-cost and scalable smartphone-based BBTs (SBBT) and – to the best of our knowledge - because it has not been investigated so far, we aim to design and evaluate the efficacy of such a SBBT. As a very first step, we apply design-science research and investigate in this research-in-progress the relationship of diaphragmatic breathing and its acoustic components by just using a smartphone’s microphone. For that purpose, we review related work and develop our hypotheses based on justificatory knowledge from physiology, physics and acoustics. We finally describe a laboratory study that is used to test our hypotheses. We conclude with a brief outlook on future work.