The mobile sleep lab app: An open-source framework for mobile sleep assessment based on consumer-grade wearable devices

Burgdorf, Andreas; Güthe, Inga; Jovanovic, Marko; Kutafina, Ekaterina; Kohlschein, Christian; Bitsch, Jó Ágila; Jonas, Stephan

doi:10.1016/j.compbiomed.2018.09.025

Cited by 21 publications

(12 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to commercial snoring apps, which are available at the common app stores, some research groups developed and tested their own smartphone apps for measurement of snoring and screening of OSA [ 25 , 26 , 35 , 36 ]. Focusing on those which solely use the smartphone without any supplementary external devices, two studies can be extrapolated [ 25 , 26 ]: Regarding the correlation between the snoring time measured by the smartphone compared to the respective gold standard, Nakano et al [ 25 ] revealed a higher correlation than the present study (r = 0.93 versus r = 0.754/0.780).…”

Section: Discussionmentioning

confidence: 99%

Accuracy of a Smartphone Application Measuring Snoring in Adults—How Smart Is It Actually?

Klaus

Stummer

Ruf

2021

IJERPH

View full text Add to dashboard Cite

About 40% of the adult population is affected by snoring, which is closely related to obstructive sleep apnea (OSA) and can be associated with serious health implications. Commercial smartphone applications (apps) offer the possibility of monitoring snoring at home. However, the number of validation studies addressing snoring apps is limited. The purpose of the present study was to assess the accuracy of recorded snoring using the free version of the app SnoreLab (Reviva Softworks Ltd., London, United Kingdom) in comparison to a full-night polygraphic measurement (Miniscreen plus, Löwenstein Medical GmbH & Co., KG, Bad Ems, Germany). Nineteen healthy adult volunteers (4 female, 15 male, mean age: 38.9 ± 19.4 years) underwent simultaneous polygraphic and SnoreLab app measurement for one night at home. Parameters obtained by the SnoreLab app were: starting/ending time of monitoring, time in bed, duration and percent of quiet sleep, light, loud and epic snoring, total snoring time and Snore Score, a specific score obtained by the SnoreLab app. Data obtained from polygraphy were: starting/ending time of monitoring, time in bed, total snoring time, snore index (SI), snore index obstructive (SI obstructive) and apnea-hypopnea-index (AHI). For different thresholds of percentage snoring per night, accuracy, sensitivity, specificity, positive and negative predictive values were calculated. Comparison of methods was undertaken by Spearman-Rho correlations and Bland-Altman plots. The SnoreLab app provides acceptable accuracy values measuring snoring >50% per night: 94.7% accuracy, 100% sensitivity, 94.1% specificity, 66.6% positive prediction value and 100% negative prediction value. Best agreement between both methods was achieved in comparing the sum of loud and epic snoring ratios obtained by the SnoreLab app with the total snoring ratio measured by polygraphy. Obstructive events could not be detected by the SnoreLab app. Compared to polygraphy, the SnoreLab app provides acceptable accuracy values regarding the measurement of especially heavy snoring.

show abstract

Section: Discussionmentioning

confidence: 99%

Accuracy of a Smartphone Application Measuring Snoring in Adults—How Smart Is It Actually?

Klaus

Stummer

Ruf

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…On the other hand, environmental sensors are those that are used to measure different properties of the environment around the smartphone. This may include a light sensor to measure the intensity of ambient light, which is commonly used to adjust the brightness of the screen [ 45 ], and the microphone, which can be used for capturing environmental sounds [ 46 ]. Other sensors, such as the GPS, may be classified as sensors that capture both the environment and movement.…”

Section: The Use Of Passive Data Collection In Smoking Cessation Appsmentioning

confidence: 99%

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Abo-Tabik

Benn

Costen

2021

Sensors

View full text Add to dashboard Cite

Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention.

show abstract

“…In a different domain, the work of Ciman and Wac (2016) presents an approach for stress assessment that leverages data extracted from smartphone sensors, and that is not invasive concerning privacy. There are also approaches for physical activities [Peart, Balsalobre and Shaw 2019] and sleeping patterns [Burgdorf 2018] assessment. However, Wac and colleagues indicate that "there is no holistic app for researchers and smartphone users to deploy and participate in evidence-based longitudinal, multidimensional studies, generating high-resolution datasets to assess and then change behaviours and improve QoL in the long-term" [Manea and Wac 2018;Wac et al 2015b].…”

Section: General Informationmentioning

confidence: 99%

Holistic Reasoning on Quality of Life Dimensions Based on Mobile apps with Ontology

Cavaco¹,

Siebra²

2020

Anais Principais Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2020)

View full text Add to dashboard Cite

To integrate the information about diverse QoL dimensions and enable the specification of a holistic process of reasoning that can improve the effectiveness of interventions, we explore the integration of different QoL aspects in the form of an ontological model, presenting associations between their concepts that should be considered together when inferences are made, since they are usually intrinsically related and would not be complete when separately analysed. These dimensions will be studied from a selected bundle of iOS and Android mobile apps within QoL aspects of Diet, Emotional,

show abstract

The mobile sleep lab app: An open-source framework for mobile sleep assessment based on consumer-grade wearable devices

Cited by 21 publications

References 26 publications

Accuracy of a Smartphone Application Measuring Snoring in Adults—How Smart Is It Actually?

Accuracy of a Smartphone Application Measuring Snoring in Adults—How Smart Is It Actually?

Are Machine Learning Methods the Future for Smoking Cessation Apps?

Holistic Reasoning on Quality of Life Dimensions Based on Mobile apps with Ontology

Contact Info

Product

Resources

About