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Peter Tinschert

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Tinschert

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Peter

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+41 71 224 7256

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Nighttime Continuous Contactless Smartphone-Based Cough Monitoring for the Ward: Validation Study
(JMIR Publications, 2023-02-20)
Barata, Filipe
;
Cleres, David
;
Tinschert, Peter
;
Shih, Chen-Hsuan Iris
;
Rassouli, Frank
Type:journal article
Journal:JMIR FORMATIVE RESEARCH
Volume:JMIR Form Res 2023;7:e38439
URL:https://formative.jmir.org/2023/1/e38439
DOI:10.2196/38439
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/107700
Scopus© Citations 26
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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
;
Tinschert, Peter
;
Rassouli, Frank
;
Steurer-Stey, Claudia
;
Fleisch, Elgar
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.
Type:journal article
Journal:Journal of Medical Internet Research
Volume:22
Issue:7
URL:https://doi.org/10.2196/18082
DOI:10.2196/18082
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/111987
Scopus© Citations 46
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Characteristics of Asthma-related Nocturnal Cough: A Potential New Digital Biomarker
(Dove Medical Press, 2020-12-03)
Rassouli, Frank
;
Tinschert, Peter
;
Barata, Filipe
;
Steurer-Stey, Claudia
;
Fleisch, Elgar
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.
Type:journal article
Journal:Journal of Asthma and Allergy
Volume:12
Issue:December
URL:https://doi.org/10.2147/JAA.S278119
DOI:10.2147/JAA.S278119
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/111486
Scopus© Citations 24
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Nocturnal cough and sleep quality to assess asthma control and predict attacks
(Dove Medical Press, 2020-12-14)
Tinschert, Peter
;
Rassouli, Frank
;
Barata, Filipe
;
Steurer-Stey, Claudia
;
Fleisch, Elgar
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.
Type:journal article
Journal:Journal of Asthma and Allergy
Volume:13
URL:https://doi.org/10.2147/JAA.S278155
DOI:10.2147/JAA.S278155
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/111470
Scopus© Citations 32
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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, Multi-Centre, Longitudinal Observational Study with Two Stages
(2019-01-07)
Tinschert, Peter
;
Rassouli, Frank
;
Barata, Filipe
;
Steurer-Stey, Claudia
;
Fleisch, Elgar
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.
Type:journal article
Journal:BMJ Open
DOI:10.1136/bmjopen-2018-026323
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/98993
Scopus© Citations 23
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The Potential of Mobile Apps for Improving Asthma Self-Management: A Review of Publicly Available and Well-Adopted Asthma Apps
(2017)
Tinschert, Peter
;
Jakob, Robert
;
Barata, Filipe
;
Kramer, Jan-Niklas
;
Kowatsch, Tobias
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.
Type:journal article
Journal:JMIR mHealth and uHealth
Volume:5
Issue:8
DOI:10.2196/mhealth.7177
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/103525
Scopus© Citations 134

Peter Tinschert

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