Pub Date : 2023-09-18DOI: 10.1186/s44247-023-00034-z
Summer Mengelkoch, Matthew Espinosa, Stephen A. Butler, Laura Joigneau Prieto, Emma Russell, Chris Ramshaw, Shardi Nahavandi, Sarah E. Hill
Abstract Background Digital decision aids are becoming increasingly common in many areas of healthcare. These aids are designed to involve patients in medical decision making, with the aim of improving patient outcomes while decreasing healthcare burden. Previously developed contraceptive-based decision aids have been found to be effective at increasing women’s knowledge about reproductive health and contraception. Here, we sought to evaluate the effectiveness of a novel contraceptive-based decision aid at increasing women’s self-efficacy and knowledge about their reproductive health and contraceptive options, as well as their perceptions of their learning. This study was registered as a clinic trial at ClinicalTrials.gov (Contraception Decision Aid Use and Patient Outcomes, ID# NCT05177783) on 05/01/2022. Methods The Tuune® contraceptive decision aid’s effectiveness was evaluated by conducting an experiment in which 324 women were assigned to use the Tuune® decision aid or a control decision aid. Primary outcomes included reproductive health self-efficacy, reproductive health and contraceptive knowledge, and perceptions of learning. Secondary analyses examined whether prior experience using hormonal contraceptives moderated the relationship between decision aid and each outcome measure. Results Women assigned to use the Tuune® decision aid exhibited greater reproductive health self-efficacy, greater knowledge about reproductive health and contraception, and perceived having learned more than women assigned to use the control decision aid ( p s ≤ .029). This pattern was also observed in women with previous contraceptive use experience, where women using Tuune® reported better outcomes than women using the control aid, regardless of their history of hormonal contraceptive use experience, although this interaction was not significant ( p = .089). Conclusions Use of the Tuune® contraceptive-based decision aid improved each of the predicted outcomes relative to a control decision aid. This suggests that use of the Tuune® contraceptive-based decision aid is well poised to increase women’s confidence and knowledge about contraceptive use and may also reduce burden on healthcare systems.
{"title":"Tuuned in: use of an online contraceptive decision aid for women increases reproductive self-efficacy and knowledge; results of an experimental clinical trial","authors":"Summer Mengelkoch, Matthew Espinosa, Stephen A. Butler, Laura Joigneau Prieto, Emma Russell, Chris Ramshaw, Shardi Nahavandi, Sarah E. Hill","doi":"10.1186/s44247-023-00034-z","DOIUrl":"https://doi.org/10.1186/s44247-023-00034-z","url":null,"abstract":"Abstract Background Digital decision aids are becoming increasingly common in many areas of healthcare. These aids are designed to involve patients in medical decision making, with the aim of improving patient outcomes while decreasing healthcare burden. Previously developed contraceptive-based decision aids have been found to be effective at increasing women’s knowledge about reproductive health and contraception. Here, we sought to evaluate the effectiveness of a novel contraceptive-based decision aid at increasing women’s self-efficacy and knowledge about their reproductive health and contraceptive options, as well as their perceptions of their learning. This study was registered as a clinic trial at ClinicalTrials.gov (Contraception Decision Aid Use and Patient Outcomes, ID# NCT05177783) on 05/01/2022. Methods The Tuune® contraceptive decision aid’s effectiveness was evaluated by conducting an experiment in which 324 women were assigned to use the Tuune® decision aid or a control decision aid. Primary outcomes included reproductive health self-efficacy, reproductive health and contraceptive knowledge, and perceptions of learning. Secondary analyses examined whether prior experience using hormonal contraceptives moderated the relationship between decision aid and each outcome measure. Results Women assigned to use the Tuune® decision aid exhibited greater reproductive health self-efficacy, greater knowledge about reproductive health and contraception, and perceived having learned more than women assigned to use the control decision aid ( p s ≤ .029). This pattern was also observed in women with previous contraceptive use experience, where women using Tuune® reported better outcomes than women using the control aid, regardless of their history of hormonal contraceptive use experience, although this interaction was not significant ( p = .089). Conclusions Use of the Tuune® contraceptive-based decision aid improved each of the predicted outcomes relative to a control decision aid. This suggests that use of the Tuune® contraceptive-based decision aid is well poised to increase women’s confidence and knowledge about contraceptive use and may also reduce burden on healthcare systems.","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135110872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-12DOI: 10.1186/s44247-023-00045-w
Thomas Wetere Tulu, Tsz Kin Wan, Ching Long Chan, Chun Hei Wu, Peter Yat Ming Woo, Cee Zhung Steven Tseng, Asmir Vodencarevic, Cristina Menni, Kei Hang Katie Chan
{"title":"Correction: Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers","authors":"Thomas Wetere Tulu, Tsz Kin Wan, Ching Long Chan, Chun Hei Wu, Peter Yat Ming Woo, Cee Zhung Steven Tseng, Asmir Vodencarevic, Cristina Menni, Kei Hang Katie Chan","doi":"10.1186/s44247-023-00045-w","DOIUrl":"https://doi.org/10.1186/s44247-023-00045-w","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135878150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.1186/s44247-023-00035-y
Shaina Raza, Elham Dolatabadi, Nancy Ondrusek, Laura Rosella, Brian Schwartz
Abstract Background Social determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available in electronic health records, clinical reports, and social media data, usually in free text format. Extracting key information from free text poses a significant challenge and necessitates the use of natural language processing (NLP) techniques to extract key information. Objective The objective of this research is to advance the automatic extraction of SDOH from clinical texts. Setting and data The case reports of COVID-19 patients from the published literature are curated to create a corpus. A portion of the data is annotated by experts to create ground truth labels, and semi-supervised learning method is used for corpus re-annotation. Methods An NLP framework is developed and tested to extract SDOH from the free texts. A two-way evaluation method is used to assess the quantity and quality of the methods. Results The proposed NER implementation achieves an accuracy (F1-score) of 92.98% on our test set and generalizes well on benchmark data. A careful analysis of case examples demonstrates the superiority of the proposed approach in correctly classifying the named entities. Conclusions NLP can be used to extract key information, such as SDOH factors from free texts. A more accurate understanding of SDOH is needed to further improve healthcare outcomes.
{"title":"Discovering social determinants of health from case reports using natural language processing: algorithmic development and validation","authors":"Shaina Raza, Elham Dolatabadi, Nancy Ondrusek, Laura Rosella, Brian Schwartz","doi":"10.1186/s44247-023-00035-y","DOIUrl":"https://doi.org/10.1186/s44247-023-00035-y","url":null,"abstract":"Abstract Background Social determinants of health are non-medical factors that influence health outcomes (SDOH). There is a wealth of SDOH information available in electronic health records, clinical reports, and social media data, usually in free text format. Extracting key information from free text poses a significant challenge and necessitates the use of natural language processing (NLP) techniques to extract key information. Objective The objective of this research is to advance the automatic extraction of SDOH from clinical texts. Setting and data The case reports of COVID-19 patients from the published literature are curated to create a corpus. A portion of the data is annotated by experts to create ground truth labels, and semi-supervised learning method is used for corpus re-annotation. Methods An NLP framework is developed and tested to extract SDOH from the free texts. A two-way evaluation method is used to assess the quantity and quality of the methods. Results The proposed NER implementation achieves an accuracy (F1-score) of 92.98% on our test set and generalizes well on benchmark data. A careful analysis of case examples demonstrates the superiority of the proposed approach in correctly classifying the named entities. Conclusions NLP can be used to extract key information, such as SDOH factors from free texts. A more accurate understanding of SDOH is needed to further improve healthcare outcomes.","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135938654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1186/s44247-023-00036-x
M. Matre, T. Johansen, A. Olsen, S. Tornås, AC Martinsen, A. Lund, F. Becker, C. Brunborg, J. Spikman, J. Ponsford, D. Neumann, S. McDonald, M. Løvstad
{"title":"A protocol for the development and validation of a virtual reality-based clinical test of social cognition","authors":"M. Matre, T. Johansen, A. Olsen, S. Tornås, AC Martinsen, A. Lund, F. Becker, C. Brunborg, J. Spikman, J. Ponsford, D. Neumann, S. McDonald, M. Løvstad","doi":"10.1186/s44247-023-00036-x","DOIUrl":"https://doi.org/10.1186/s44247-023-00036-x","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48323806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-07DOI: 10.1186/s44247-023-00037-w
Lauri Lukka, Antti Salonen, M. Vesterinen, Veli-Matti Karhulahti, S. Palva, J. Palva
{"title":"The qualities of patients interested in using a game-based digital mental health intervention for depression: a sequential mixed methods study","authors":"Lauri Lukka, Antti Salonen, M. Vesterinen, Veli-Matti Karhulahti, S. Palva, J. Palva","doi":"10.1186/s44247-023-00037-w","DOIUrl":"https://doi.org/10.1186/s44247-023-00037-w","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42471605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-05DOI: 10.1186/s44247-023-00033-0
Megan M. MacPherson, Shabana Kapadia
{"title":"Barriers and facilitators to patient-to-provider messaging using the COM-B model and theoretical domains framework: a rapid umbrella review","authors":"Megan M. MacPherson, Shabana Kapadia","doi":"10.1186/s44247-023-00033-0","DOIUrl":"https://doi.org/10.1186/s44247-023-00033-0","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44938484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04DOI: 10.1186/s44247-023-00032-1
M. Geldof, Nina Gerlach, A. Portela
{"title":"Digitalization of home-based records for maternal, newborn, and child health: a scoping review","authors":"M. Geldof, Nina Gerlach, A. Portela","doi":"10.1186/s44247-023-00032-1","DOIUrl":"https://doi.org/10.1186/s44247-023-00032-1","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42413801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-21DOI: 10.1186/s44247-023-00031-2
M. Coleshill, D. Alagirisamy, S. Patki, M. Ronksley, M. Black, S. Yu, M. Phillips, J. Newby, N. Cockayne, J. Tennant, S. Harvey, H. Christensen, P. Baldwin
{"title":"The Essential Network (TEN): engagement and mental health insights from a digital mental health assessment tool for Australian health professionals during COVID-19","authors":"M. Coleshill, D. Alagirisamy, S. Patki, M. Ronksley, M. Black, S. Yu, M. Phillips, J. Newby, N. Cockayne, J. Tennant, S. Harvey, H. Christensen, P. Baldwin","doi":"10.1186/s44247-023-00031-2","DOIUrl":"https://doi.org/10.1186/s44247-023-00031-2","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49136634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-10DOI: 10.1186/s44247-023-00030-3
C. Bennett, Massirfufulay Kpehe Musa, Judith Carrier, D. Edwards, Elizabeth Gillen, A. Sydor, Catherine Dunn, Kaye Jones-Mahoney, Alex Nute, D. Kelly
{"title":"The barriers and facilitators to young people’s engagement with bidirectional digital sexual health interventions: a mixed methods systematic review","authors":"C. Bennett, Massirfufulay Kpehe Musa, Judith Carrier, D. Edwards, Elizabeth Gillen, A. Sydor, Catherine Dunn, Kaye Jones-Mahoney, Alex Nute, D. Kelly","doi":"10.1186/s44247-023-00030-3","DOIUrl":"https://doi.org/10.1186/s44247-023-00030-3","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45053517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-03DOI: 10.1186/s44247-023-00028-x
J. Frost, Charley Hobson-Merrett, L. Gask, Mike Clark, V. Pinfold, H. Plappert, S. Reilly, J. Gibson, Debra Richards, Rebecca Denyer, R. Byng
{"title":"Liquidity and uncertainty: digital adaptation of a complex intervention for people with severe mental illness during the COVID-19 lockdown","authors":"J. Frost, Charley Hobson-Merrett, L. Gask, Mike Clark, V. Pinfold, H. Plappert, S. Reilly, J. Gibson, Debra Richards, Rebecca Denyer, R. Byng","doi":"10.1186/s44247-023-00028-x","DOIUrl":"https://doi.org/10.1186/s44247-023-00028-x","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42116517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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