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  • 1
    In: Journal of Sleep Research, Wiley
    Abstract: Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter‐rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ  = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep ( κ  = 0.86) and lowest in N1 sleep ( κ  = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage‐specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ  = 0.79, κ  = 0.85, and κ  = 0.78, while the lowest pairwise agreement between the scorers was κ  = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea–hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non‐rapid eye movement stages.
    Type of Medium: Online Resource
    ISSN: 0962-1105 , 1365-2869
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2007459-1
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  • 2
    In: Frontiers in Neuroinformatics, Frontiers Media SA, Vol. 18 ( 2024-5-13)
    Abstract: Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to how exactly the algorithms should be incorporated into the workflow of sleep technologists. This paper presents a sophisticated data collection platform developed under the Sleep Revolution project, to harness polysomnographic data from multiple European centers. Methods A tripartite platform is presented: a user-friendly web platform for uploading three-night polysomnographic recordings, a dedicated splitter that segments these into individual one-night recordings, and an advanced processor that enhances the one-night polysomnography with contemporary automatic scoring algorithms. The platform is evaluated using real-life data and human scorers, whereby scoring time, accuracy, and trust are quantified. Additionally, the scorers were interviewed about their trust in the platform, along with the impact of its integration into their workflow. Results We found that incorporating AI into the workflow of sleep technologists both decreased the time to score by up to 65 min and increased the agreement between technologists by as much as 0.17 κ . Discussion We conclude that while the inclusion of AI into the workflow of sleep technologists can have a positive impact in terms of speed and agreement, there is a need for trust in the algorithms.
    Type of Medium: Online Resource
    ISSN: 1662-5196
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2024
    detail.hit.zdb_id: 2452979-5
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  • 3
    In: Frontiers in Sleep, Frontiers Media SA, Vol. 2 ( 2023-2-17)
    Abstract: Sleep-disordered breathing (SDB) can range from habitual snoring to severe obstructive sleep apnea (OSA). A common characteristic of SDB in children is mouth breathing, yet it is commonly overlooked and inconsistently diagnosed. The primary aim of this study is to construct a deep learning algorithm in order to automatically detect mouth breathing events in children from polysomnography (PSG) recordings. Methods The PSG of 20 subjects aged 10–13 years were used, 15 of which had reported snoring or presented high snoring and/or high OSA values by scoring conducted by a sleep technologist, including mouth breathing events. The separately measured mouth and nasal pressure signals from the PSG were fed through convolutional neural networks to identify mouth breathing events. Results The finalized model presented 93.5% accuracy, 97.8% precision, 89% true positive rate, and 2% false positive rate when applied to the validation data that was set aside from the training data. The model's performance decreased when applied to a second validation data set, indicating a need for a larger training set. Conclusion The results show the potential of deep neural networks in the analysis and classification of biological signals, and illustrates the usefulness of machine learning in sleep analysis.
    Type of Medium: Online Resource
    ISSN: 2813-2890
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 3148288-0
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  • 4
    In: JMIR Formative Research, JMIR Publications Inc., Vol. 7 ( 2023-4-28), p. e39331-
    Abstract: Inflammatory bowel disease (IBD) causes chronic inflammation of the gastrointestinal tract. IBD is characterized by an unpredictable disease course that varies greatly between individuals and alternates between the periods of relapse and remission. A low energy level (fatigue) is a common symptom, whereas stress and reduced sleep quality may be the triggering factors. Therapeutic guidelines call for effective disease assessment, early intervention, and personalized care using a treat-to-target approach, which may be difficult to achieve through typical time- and resource-constrained standard care. Providing patients with a digital health program that incorporates helpful self-management features and patient support to complement standard care may be optimal for improving the disease course. Objective This study aimed to perform a preliminary program evaluation, analyzing engagement and preliminary effectiveness and the effect on participants’ energy levels (fatigue), stress, and sleep quality, of a newly developed 16-week digital health program (SK-311 and SK-321) for patients with IBD. Methods Adults with IBD were recruited to participate in a real-world, live, digital health program via Finnish IBD patient association websites and social media. No inclusion or exclusion criteria were applied for this study. Baseline characteristics were entered by the participants upon sign-up. Platform engagement was measured by tracking the participants’ event logs. The outcome measures of stress, energy levels (fatigue), and quality of sleep were reported by participants through the platform. Results Of the 444 adults who registered for the digital health program, 205 (46.2%) were included in the intention-to-treat sample. The intention-to-treat participants logged events on average 41 times per week (5.9 times per day) during the weeks in which they were active on the digital platform. More women than men participated in the intervention (126/205, 88.7%). The mean age of the participants was 40.3 (SD 11.5) years, and their mean BMI was 27.9 (SD 6.0) kg/m2. In total, 80 people provided the required outcome measures during weeks 12 to 16 (completers). Treatment completion was strongly predicted by the number of active days in week 1. Analysis of the completers (80/205, 39%) showed significant improvements for stress (t79=4.57; P 〈 .001; percentage change=−23.26%) and energy levels (t79=−2.44; P=.017; percentage change=9.48%); however, no significant improvements were observed for quality of sleep (t79=−1.32; P=.19). Conclusions These results support the feasibility of a digital health program for patients with IBD (SK-311 and SK-321) and suggest that treatment completion might have a substantial positive effect on patient-reported stress and energy levels in a real-world setting. These findings are promising and provide initial support for using the Sidekick Health digital health program to supplement standard care for patients with IBD.
    Type of Medium: Online Resource
    ISSN: 2561-326X
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2023
    detail.hit.zdb_id: 2941716-8
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  • 5
    In: Sleep Medicine Reviews, Elsevier BV, Vol. 73 ( 2024-02), p. 101874-
    Type of Medium: Online Resource
    ISSN: 1087-0792
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2010032-2
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  • 6
    Online Resource
    Online Resource
    Emerald ; 2022
    In:  Journal of Workplace Learning Vol. 35, No. 9 ( 2022-09-29), p. 22-37
    In: Journal of Workplace Learning, Emerald, Vol. 35, No. 9 ( 2022-09-29), p. 22-37
    Abstract: This study aims to explore virtual leadership work within educational settings in the light of social disruption. In 2020, a global pandemic changed the way we work. For school leaders, that involved running a virtual school overnight. Although there is a stream of research that explores leadership in solely virtual communities, there is a gap in the literature regarding practices that transition from analog to virtual practices and the changes in leadership in those types of work practices. Design/methodology/approach The data gathering method constitutes a questionnaire to explore school leaders’ experiences of virtual work and virtual leadership in light of social disruption. One hundred and five Swedish school leaders answered the questionnaire covering both fixed and open questions. Findings The results show that school leaders’ general experiences of transition to virtual school have worked relatively well. We show how the work changes and shift the focus in the virtual workplace. Originality/value The author’s contributions include theorizing about leadership affordances in virtual schools and providing implications for practice. The authors summarize our main contribution in five affordances that characterize virtual leadership, including a focus on core activities, trust-based government, 1:1 communication with staff, structure and clarity and active outreach activities. The results could be interesting for understanding the radical digitalization of leadership in the digital workplace.
    Type of Medium: Online Resource
    ISSN: 1366-5626 , 1366-5626
    Language: English
    Publisher: Emerald
    Publication Date: 2022
    detail.hit.zdb_id: 2020814-5
    SSG: 3,2
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  • 7
    Online Resource
    Online Resource
    Emerald ; 2017
    In:  Journal of Workplace Learning Vol. 29, No. 7/8 ( 2017-09-11), p. 577-587
    In: Journal of Workplace Learning, Emerald, Vol. 29, No. 7/8 ( 2017-09-11), p. 577-587
    Abstract: The aim of this paper is to understand how the role of an mHealth artifact plays out in home care settings. An mHealth artifact, in terms of a mobile app, was tested to see how the quality of home care work practice was enhanced and changed. The research question is: In what ways does an mHealth artifact re-shape a home care practice and how does this affect the interaction between caregivers and the elderly and learning opportunities for the caregivers? Design/methodology/approach An action research approach was taken and the study was conducted in a home care organization in a Swedish municipality. The data were collected through semi-structured interviews and observations that were conducted during home visits. Concepts of learning and boundary objects were used to analyze and distinguish interactions and conversations with the mHealth artifact. Findings The study shows how an mHealth artifact is re-shaping a home care practice and how this affects interactions and identifies learning opportunities. Views on the mHealth artifact as a designated boundary object as well as a boundary object-in-use must co-exist. Originality/value The study provides qualitative descriptions from using an mHealth artifact for home care, which is an emerging area of concern for both research and practice. It focuses on the interactional and organizational values generated from the actual use of the designed mobile application.
    Type of Medium: Online Resource
    ISSN: 1366-5626
    Language: English
    Publisher: Emerald
    Publication Date: 2017
    detail.hit.zdb_id: 2020814-5
    SSG: 3,2
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  • 8
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Current Sleep Medicine Reports Vol. 9, No. 2 ( 2023-04-10), p. 91-100
    In: Current Sleep Medicine Reports, Springer Science and Business Media LLC, Vol. 9, No. 2 ( 2023-04-10), p. 91-100
    Abstract: The complexity of the data collected for sleep research is increasing, and the focal point of sleep research is dependent on a higher number of data sources. Data collected for sleep studies often includes both subjective and objective measurements of sleep quality and is gathered over a more extended period, e.g., for weeks, months, or even years. However, this variety and volume of data make it challenging and time-consuming for researchers to utilize. Therefore, sophisticated data structures are necessary to utilize data in sleep research. Recent Findings This paper explores how heterogeneous data sources can be represented in a homogeneous database design. The following research questions drove our work: (i) How can we represent sleep data from heterogeneous sources in a homogenous digital platform database? and (ii) How can a data source pipeline transform various data sources into a homogeneous data format? Summary This paper’s main contributions are conceptualizing the design and development of a homogeneous database and digital platform architecture and a data source pipeline that fits well for sleep research in particular and healthcare research in general.
    Type of Medium: Online Resource
    ISSN: 2198-6401
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2806592-X
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  • 9
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2023
    In:  Current Sleep Medicine Reports Vol. 9, No. 3 ( 2023-06-30), p. 140-151
    In: Current Sleep Medicine Reports, Springer Science and Business Media LLC, Vol. 9, No. 3 ( 2023-06-30), p. 140-151
    Abstract: Automatic analysis of sleep is an important and active area of research. Machine learning models are commonly developed to classify time segments into sleep stages. The sleep stages can be used to calculate various sleep parameters, such as sleep efficiency and total sleep time. The machine learning models are typically trained to minimize the sleep stage classification error, but little is known about how error propagates from sleep stages to derived sleep parameters. Recent findings: We review recently published studies where machine learning was used to classify sleep stages using data from wearable devices. Using classification error statistics from these studies, we perform a Monte Carlo simulation to estimate sleep parameter error in a dataset of 197 hypnograms. This is, to our knowledge, the first attempt at evaluating how robust sleep parameter estimation is to misclassification of sleep stages. Summary: Our analysis suggests that a machine learning model capable of 90% accurate sleep stage classification (surpassing current state-of-art in wearable sleep tracking) may perform worse than a random guess in estimating some sleep parameters. Our analysis also indicates that sleep stage classification may not be a relevant target variable for machine learning on wearable sleep data and that regression models may be better suited to estimating sleep parameters. Finally, we propose a baseline model to use as a reference for sleep stage estimation accuracy. When applied to a test set, the baseline model predicts 2-, 3-, 4- and 5-class sleep stages with an accuracy of 74%, 54%, 46% and 35%, respectively
    Type of Medium: Online Resource
    ISSN: 2198-6401
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2806592-X
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  • 10
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2019
    In:  Computer Supported Cooperative Work (CSCW) Vol. 28, No. 3-4 ( 2019-6), p. 435-468
    In: Computer Supported Cooperative Work (CSCW), Springer Science and Business Media LLC, Vol. 28, No. 3-4 ( 2019-6), p. 435-468
    Type of Medium: Online Resource
    ISSN: 0925-9724 , 1573-7551
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 1479849-9
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