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- Bäckström, Caroline A., et al.
(author)
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Healthcare professionals' perceptions of a digital parental support, Childbirth Journey, constructed as a serious game—An intervention study
- 2023
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In: Frontiers in Digital Health. - : Frontiers Media S.A.. - 2673-253X. ; 5
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Journal article (peer-reviewed)abstract
- Background: Globally, the digital sources developed and available in antenatal care differ, and infrastructure challenges may impede the further development of such sources. Challenges accompanying digital developments can include the commonly occurring high workload, which affects healthcare professionals' ability to acquire professional knowledge about how to best support parents in using digital sources. Including healthcare professionals in the development process of digital sources may increase the likelihood that such sources will be adopted and employed by these professionals in their future care work. Therefore, the present research explored healthcare professionals' perceptions of the digital support intervention Childbirth Journey, which was constructed as a serious game for expectant parents. Methods: Data were collected through semi-structured focus-group interviews with 11 midwives at antenatal, labour and postnatal clinics as well as with child healthcare nurses. Prior to the interviews, all participants were provided the intervention, Childbirth Journey, which is a serious game in a mobile application format consisting of two distinct parts: (1) a story-driven game and (2) a Knowledge Portal. The data were analysed using phenomenographic methods. Results: The perceptions of Childbirth Journey by healthcare professionals, midwives and child healthcare nurses are presented in four descriptive categories: extended professional support, trustworthy contents, diversity or individuality, and both appealing and in need of development. Conclusions: Current study revealed that Childbirth Journey may be utilised as a digital support for parents, allowing healthcare professionals to offer a digital solution as a complementary support to standard, face-to-face meetings with caregivers. However, the research results also revealed that some elements of Childbirth Journey must be improved, thereby representing a main contribution of this study: insights into how to better develop digital tools under the umbrella of health care. Thus, we conclude that in order to create sustainable and safe digital care solutions that function as trustworthy professional supports instead of technical products that risk harming users, the perspectives of both patients and healthcare professionals should be considered in the exploration and development of these solutions.
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| 2. |
- Hamidi, Farzaneh, et al.
(author)
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Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach : application of Boruta
- 2023
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In: FRONTIERS IN DIGITAL HEALTH. - : Frontiers Media S.A.. - 2673-253X. ; 5
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Journal article (peer-reviewed)abstract
- Introduction: In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult.Methods: By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost.Results: Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.
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| 3. |
- Jingili, Nuru, et al.
(author)
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A systematic review on the efficacy of virtual reality and gamification interventions for managing anxiety and depression
- 2023
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In: Frontiers in Digital Health. - : Frontiers Media S.A.. - 2673-253X. ; 5
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Journal article (peer-reviewed)abstract
- This systematic review aims to assess the effectiveness of virtual reality (VR) and gamification interventions in addressing anxiety and depression. The review also seeks to identify gaps in the current VR treatment landscape and provide guidelines for future research and development. A systematic literature search was conducted using Scopus, Web of Science, and PubMed databases, focusing on studies that utilized VR and gamification technology to address anxiety and depression disorders. A total of 2,664 studies were initially identified, 15 of those studies fulfilled the inclusion criteria for this systematic review. The efficacy of VR in addressing anxiety and depression was evident across all included studies. However, the diversity among VR interventions highlights the need for further investigation. It is advised to incorporate more diverse participant samples and larger cohorts and explore a broader spectrum of therapeutic approaches within VR interventions for addressing anxiety and depression to enhance the credibility of future research. Additionally, conducting studies in varying socioeconomic contexts would contribute to a more comprehensive understanding of their real-world applicability.
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| 4. |
- Leijon, Arne, et al.
(author)
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Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation
- 2023
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In: Frontiers in Digital Health. - : Frontiers Media SA. - 2673-253X. ; 5
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Journal article (peer-reviewed)abstract
- This paper presents a new Bayesian method for analyzing Ecological Momentary Assessment (EMA) data and applies this method in a re-analysis of data from a previous EMA study. The analysis method has been implemented as a freely available Python package EmaCalc, RRID:SCR 022943. The analysis model can use EMA input data including nominal categories in one or more situation dimensions, and ordinal ratings of several perceptual attributes. The analysis uses a variant of ordinal regression to estimate the statistical relation between these variables. The Bayesian method has no requirements related to the number of participants or the number of assessments by each participant. Instead, the method automatically includes measures of the statistical credibility of all analysis results, for the given amount of data. For the previously collected EMA data, the analysis results demonstrate how the new tool can handle heavily skewed, scarce, and clustered data that were collected on ordinal scales, and present results on interval scales. The new method revealed results for the population mean that were similar to those obtained in the previous analysis by an advanced regression model. The Bayesian approach automatically estimated the inter-individual variability in the population, based on the study sample, and could show some statistically credible intervention results also for an unseen random individual in the population. Such results may be interesting, for example, if the EMA methodology is used by a hearing-aid manufacturer in a study to predict the success of a new signal-processing method among future potential customers.
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