V. Dzomeku, Adwoa Bemah Boamah Mensah, E. Nakua, P. Agbadi, J. Okyere, Alex Kumah, Jacob Munukpa, A. Ofosu, N. Lockhart, J. Lori
{"title":"Feasibility of the use of WhatsApp messaging technology to facilitate obstetric referrals in rural Ghana","authors":"V. Dzomeku, Adwoa Bemah Boamah Mensah, E. Nakua, P. Agbadi, J. Okyere, Alex Kumah, Jacob Munukpa, A. Ofosu, N. Lockhart, J. Lori","doi":"10.1186/s44247-023-00012-5","DOIUrl":"https://doi.org/10.1186/s44247-023-00012-5","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41812294","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}
K. Engan, Øyvind Meinich-Bache, Sara Brunner, H. Myklebust, Chunming Rong, Jorge García-Torres, H. Ersdal, Anders Johannessen, Hanne Pike, S. Rettedal
{"title":"Newborn Time - improved newborn care based on video and artificial intelligence - study protocol","authors":"K. Engan, Øyvind Meinich-Bache, Sara Brunner, H. Myklebust, Chunming Rong, Jorge García-Torres, H. Ersdal, Anders Johannessen, Hanne Pike, S. Rettedal","doi":"10.1186/s44247-023-00010-7","DOIUrl":"https://doi.org/10.1186/s44247-023-00010-7","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45524593","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}
{"title":"Impact of digital health technology on health insurance claims rejection rate in Ghana: a quasi-experimental study","authors":"Godwin Adzakpah, D. Dwomoh","doi":"10.1186/s44247-023-00006-3","DOIUrl":"https://doi.org/10.1186/s44247-023-00006-3","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45110786","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}
L. Schreurs, I. Steenhout, J. Bosmans, R. Buyl, D. De Cock
{"title":"Can mHealth bridge the digital divide in rheumatic and musculoskeletal conditions?","authors":"L. Schreurs, I. Steenhout, J. Bosmans, R. Buyl, D. De Cock","doi":"10.1186/s44247-022-00005-w","DOIUrl":"https://doi.org/10.1186/s44247-022-00005-w","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41419816","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}
H. Shin, Keri Durocher, B. Lo, Sheng Chen, Clement Ma, D. Wiljer, G. Strudwick
{"title":"Impact of a mental health patient portal on patients’ views of compassion: a mixed-methods study","authors":"H. Shin, Keri Durocher, B. Lo, Sheng Chen, Clement Ma, D. Wiljer, G. Strudwick","doi":"10.1186/s44247-022-00002-z","DOIUrl":"https://doi.org/10.1186/s44247-022-00002-z","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43224128","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}
BMC digital healthPub Date : 2023-01-01Epub Date: 2023-01-24DOI: 10.1186/s44247-022-00004-x
Alison Cuff
{"title":"The evolution of digital health and its continuing challenges.","authors":"Alison Cuff","doi":"10.1186/s44247-022-00004-x","DOIUrl":"10.1186/s44247-022-00004-x","url":null,"abstract":"","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9872053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46036285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BMC digital healthPub Date : 2023-01-01Epub Date: 2023-02-14DOI: 10.1186/s44247-023-00008-1
Zhe He, Michael Dieciuc, Dawn Carr, Shayok Chakraborty, Ankita Singh, Ibukun E Fowe, Shenghao Zhang, Mia Liza A Lustria, Antonio Terracciano, Neil Charness, Walter R Boot
{"title":"New Opportunities for the Early Detection and Treatment of Cognitive Decline: Adherence Challenges and the Promise of Smart and Person-Centered Technologies.","authors":"Zhe He, Michael Dieciuc, Dawn Carr, Shayok Chakraborty, Ankita Singh, Ibukun E Fowe, Shenghao Zhang, Mia Liza A Lustria, Antonio Terracciano, Neil Charness, Walter R Boot","doi":"10.1186/s44247-023-00008-1","DOIUrl":"10.1186/s44247-023-00008-1","url":null,"abstract":"<p><p>Early detection of age-related cognitive decline has transformative potential to advance the scientific understanding of cognitive impairments and possible treatments by identifying relevant participants for clinical trials. Furthermore, early detection is also key to early intervention once effective treatments have been developed. Novel approaches to the early detection of cognitive decline, for example through assessments administered via mobile apps, may require frequent home testing which can present adherence challenges. And, once decline has been detected, treatment might require frequent engagement with behavioral and/or lifestyle interventions (e.g., cognitive training), which present their own challenges with respect to adherence. We discuss state-of-the-art approaches to the early detection and treatment of cognitive decline, adherence challenges associated with these approaches, and the promise of smart and person-centered technologies to tackle adherence challenges. Specifically, we highlight prior and ongoing work conducted as part of the <i>Adherence Promotion with Person-centered Technology</i> (APPT) project, and how completed work will contribute to the design and development of a just-in-time, tailored, smart reminder system that infers participants' contexts and motivations, and how ongoing work might build toward a reminder system that incorporates dynamic machine learning algorithms capable of predicting and preventing adherence lapses before they happen. APPT activities and findings will have implications not just for cognitive assessment and training, but for technology-mediated adherence-support systems to facilitate physical exercise, nutrition, medication management, telehealth, and social connectivity, with the potential to broadly improve the engagement, health, and well-being of older adults.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11908691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44032061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BMC digital healthPub Date : 2023-01-01Epub Date: 2023-06-16DOI: 10.1186/s44247-023-00020-5
Sahr Wali, Isaac Ssinabulya, Cinderella Ngonzi Muhangi, Jenipher Kamarembo, Jenifer Atala, Martha Nabadda, Franklin Odong, Ann R Akiteng, Heather Ross, Angela Mashford-Pringle, Joseph A Cafazzo, Jeremy I Schwartz
{"title":"Bridging community and clinic through digital health: Community-based adaptation of a mobile phone-based heart failure program for remote communities in Uganda.","authors":"Sahr Wali, Isaac Ssinabulya, Cinderella Ngonzi Muhangi, Jenipher Kamarembo, Jenifer Atala, Martha Nabadda, Franklin Odong, Ann R Akiteng, Heather Ross, Angela Mashford-Pringle, Joseph A Cafazzo, Jeremy I Schwartz","doi":"10.1186/s44247-023-00020-5","DOIUrl":"10.1186/s44247-023-00020-5","url":null,"abstract":"<p><strong>Background: </strong>In Uganda, limited healthcare access has created a significant burden for patients living with heart failure. With the increasing use of mobile phones, digital health tools could offer an accessible platform for individualized care support. In 2016, our multi-national team adapted a mobile phone-based program for heart failure self-care to the Ugandan context and found that patients using the system showed improvements in their symptoms and quality of life. With approximately 84% of Ugandans residing in rural communities, the Medly Uganda program can provide greater benefit for communities in rural areas with limited access to care. To support the implementation of this program within rural communities, this study worked in partnership with two remote clinics in Northern Uganda to identify the cultural and service level requirements for the program.</p><p><strong>Methods: </strong>Using the principles from community-based research and user-centered design, we conducted a mixed-methods study composed of 4 participatory consensus cycles, 60 semi-structured interviews (SSI) and 8 iterative co-design meetings at two remote cardiac clinics. Patient surveys were also completed during each SSI to collect data related to cell phone access, community support, and geographic barriers. Qualitative data was analyzed using inductive thematic analysis. The Indigenous method of <i>two-eyed seeing</i> was also embedded within the analysis to help promote local perspectives regarding community care.</p><p><strong>Results: </strong>Five themes were identified. The burden of travel was recognized as the largest barrier for care, as patients were travelling up to 19 km by motorbike for clinic visits. Despite mixed views on traditional medicine, patients often turned to healers due to the cost of medication and transport. With most patients owning a non-smartphone (<i>n</i> = 29), all participants valued the use of a digital tool to improve equitable access to care. However, to sustain program usage, integrating the role of village health teams (VHTs) to support in-community follow-ups and medication delivery was recognized as pivotal.</p><p><strong>Conclusion: </strong>The use of a mobile phone-based digital health program can help to reduce the barrier of geography, while empowering remote HF self-care. By leveraging the trusted role of VHTs within the delivery of the program, this will help enable more culturally informed care closer to home.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-023-00020-5.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":"20"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11116269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48709080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaina Raza, Brian Schwartz, Sahithi Lakamana, Yao Ge, Abeed Sarker
{"title":"A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications.","authors":"Shaina Raza, Brian Schwartz, Sahithi Lakamana, Yao Ge, Abeed Sarker","doi":"10.1186/s44247-023-00029-w","DOIUrl":"https://doi.org/10.1186/s44247-023-00029-w","url":null,"abstract":"<p><strong>Background: </strong>Substance use, including the non-medical use of prescription medications, is a global health problem resulting in hundreds of thousands of overdose deaths and other health problems. Social media has emerged as a potent source of information for studying substance use-related behaviours and their consequences. Mining large-scale social media data on the topic requires the development of natural language processing (NLP) and machine learning frameworks customized for this problem. Our objective in this research is to develop a framework for conducting a content analysis of Twitter chatter about the non-medical use of a set of prescription medications.</p><p><strong>Methods: </strong>We collected Twitter data for four medications-fentanyl and morphine (opioids), alprazolam (benzodiazepine), and Adderall<sup>®</sup> (stimulant), and identified posts that indicated non-medical use using an automatic machine learning classifier. In our NLP framework, we applied supervised named entity recognition (NER) to identify other substances mentioned, symptoms, and adverse events. We applied unsupervised topic modelling to identify latent topics associated with the chatter for each medication.</p><p><strong>Results: </strong>The quantitative analysis demonstrated the performance of the proposed NER approach in identifying substance-related entities from data with a high degree of accuracy compared to the baseline methods. The performance evaluation of the topic modelling was also notable. The qualitative analysis revealed knowledge about the use, non-medical use, and side effects of these medications in individuals and communities.</p><p><strong>Conclusions: </strong>NLP-based analyses of Twitter chatter associated with prescription medications belonging to different categories provide multi-faceted insights about their use and consequences. Our developed framework can be applied to chatter about other substances. Further research can validate the predictive value of this information on the prevention, assessment, and management of these disorders.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":"1 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10577738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BMC digital healthPub Date : 2023-01-01Epub Date: 2023-04-13DOI: 10.1186/s44247-023-00011-6
Yoshiko Arima
{"title":"Effects of chest movements while sitting on Navon task performance and stress levels.","authors":"Yoshiko Arima","doi":"10.1186/s44247-023-00011-6","DOIUrl":"10.1186/s44247-023-00011-6","url":null,"abstract":"<p><strong>Background: </strong>This study explored physical activity during remote work, most of which takes place while sitting in front of a computer. The purpose of Experiment 1 was to develop a classification for body motion by creating a neural net that can distinguish among several kinds of chest movement. Experiment 2 examined the effects of chest movements on stress and performance on the Navon test to validate the model developed in Experiment 1.</p><p><strong>Method and results: </strong>The procedures for this study were as follows.Experiment 1: Creation of the body movement classification model and preliminary experiment for Experiment 2.Data from five participants were used to construct a machine-learning categorization model. The other three participants participated in a pilot study for Experiment 2.Experiment 2: Model validation and confirmation of stress measurement validity.We recruited 34 new participants to test the validity of the model developed in Experiment 1. We asked 10 of the 34 participants to retake the stress measurement since the results of the stress assessment were unreliable.Using LSTM models, we classified six categories of chest movement in Experiment 1: walking, standing up and sitting down, sitting still, rotating, swinging, and rocking. The LSTM models yielded an accuracy rate of 83.8%. Experiment 2 tested the LSTM model and found that Navon task performance correlated with swinging chest movement. Due to the limited reliability of the stress measurement results, we were unable to draw a conclusion regarding the effects of body movements on stress. In terms of cognitive performance, swinging of the chest reduced RT and increased accuracy on the Navon task (β = .015 [-.003,.054], R<sup>2</sup> = .31).</p><p><strong>Conclusions: </strong>LSTM classification successfully distinguished subtle movements of the chest; however, only swinging was related to cognitive performance. Chest movements reduced the reaction time, improving cognitive performance. However, the stress measurements were not stable; thus, we were unable to draw a clear conclusion about the relationship between body movement and stress. The results indicated that swinging of the chest improved reaction times in the Navon task, while sitting still was not related to cognitive performance or stress. The present article discusses how to collect sensor data and analyze it using machine-learning methods as well as the future applicability of measuring physical activity during remote work.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":"12"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48788077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}