BMC digital health最新文献

{"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}

{"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}

{"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}

{"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}

{"title":"A framework for multi-faceted content analysis of social media chatter regarding non-medical use of prescription medications.","authors":"Shaina Raza,&nbsp;Brian Schwartz,&nbsp;Sahithi Lakamana,&nbsp;Yao Ge,&nbsp;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^® (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}

{"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], R² = .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}

{"title":"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/s44247-022-00001-0","DOIUrl":"10.1186/s44247-022-00001-0","url":null,"abstract":"<p><p><b>COVID-19 mortality prediction</b> <b>Background</b> COVID-19 has become a major global public health problem, despite prevention and efforts. The daily number of COVID-19 cases rapidly increases, and the time and financial costs associated with testing procedure are burdensome. <b>Method</b> To overcome this, we aim to identify immunological and metabolic biomarkers to predict COVID-19 mortality using a machine learning model. We included inpatients from Hong Kong's public hospitals between January 1, and September 30, 2020, who were diagnosed with COVID-19 using RT-PCR. We developed three machine learning models to predict the mortality of COVID-19 patients based on data in their electronic medical records. We performed statistical analysis to compare the trained machine learning models which are Deep Neural Networks (DNN), Random Forest Classifier (RF) and Support Vector Machine (SVM) using data from a cohort of 5,059 patients (median age = 46 years; 49.3% male) who had tested positive for COVID-19 based on electronic health records and data from 532,427 patients as controls. <b>Result</b> We identified top 20 immunological and metabolic biomarkers that can accurately predict the risk of mortality from COVID-19 with ROC-AUC of 0.98 (95% CI 0.96-0.98). Of the three models used, our result demonstrate that the random forest (RF) model achieved the most accurate prediction of mortality among COVID-19 patients with age, glomerular filtration, albumin, urea, procalcitonin, c-reactive protein, oxygen, bicarbonate, carbon dioxide, ferritin, glucose, erythrocytes, creatinine, lymphocytes, PH of blood and leukocytes among the most important biomarkers identified. A cohort from Kwong Wah Hospital (131 patients) was used for model validation with ROC-AUC of 0.90 (95% CI 0.84-0.92). <b>Conclusion</b> We recommend physicians closely monitor hematological, coagulation, cardiac, hepatic, renal and inflammatory factors for potential progression to severe conditions among COVID-19 patients. To the best of our knowledge, no previous research has identified important immunological and metabolic biomarkers to the extent demonstrated in our study.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-022-00001-0.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45453724","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}

{"title":"Acceptability and feasibility of a mobile health application for enhancing public private mix for TB care among healthcare Workers in Southwestern Uganda.","authors":"Wilson Tumuhimbise, Daniel Atwine, Fred Kaggwa, Angella Musiimenta","doi":"10.1186/s44247-023-00009-0","DOIUrl":"10.1186/s44247-023-00009-0","url":null,"abstract":"<p><strong>Background: </strong>Mobile health interventions can potentially enhance public-private linkage for tuberculosis care. However, evidence about their acceptability and feasibility is lacking. This study sought to assess the initial acceptability and feasibility of a mobile health application for following up on presumptive tuberculosis patients referred from private to public hospitals. Twenty-two healthcare workers from three private hospitals and a public hospital in southwestern Uganda received the Tuuka mobile application for 1 month for testing. Testing focused on referring patients by healthcare workers from private hospitals and receiving referred patients by public healthcare workers and sending SMS reminders to the referred patients by filling out the digital referral forms inbuilt within the app. Study participants participated in qualitative semi-structured in-depth interviews on the acceptability and feasibility of this app. An inductive, content analytic approach, framed by the unified theory of acceptance and use of technology model, was used to analyze qualitative data. Quantitative feasibility metrics and the quantitative assessment of acceptability were analyzed descriptively using STATA.</p><p><strong>Results: </strong>Healthcare workers found the Tuuka application acceptable and feasible, with a mean total system usability scale score of 98 (SD 1.97). The majority believed that the app would help them make quicker medical decisions (91%), communicate with other healthcare workers (96%), facilitate partnerships with other hospitals (100%), and enhance quick TB case notification (96%). The application was perceived to be useful in reminding referred patients to adhere to referral appointments, notifying public hospital healthcare workers about the incoming referred patients, facilitating communication across facilities, and enhancing patient-based care.</p><p><strong>Conclusion: </strong>The Tuuka mobile health application is acceptable and feasible for following up on referred presumptive tuberculosis patients referred from private to public hospitals in southwestern Uganda. Future efforts should focus on incorporating incentives to motivate and enable sustained use among healthcare workers.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-023-00009-0.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9982777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45221681","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}

{"title":"Non-inferiority of a hybrid outpatient rehabilitation: a randomized controlled trial (HIRE, DRKS00028770).","authors":"Richard Albers, Stella Lemke, Sebastian Knapp, Gert Krischak, Matthias Bethge","doi":"10.1186/s44247-023-00013-4","DOIUrl":"10.1186/s44247-023-00013-4","url":null,"abstract":"<p><strong>Background: </strong>Physiotherapeutic telerehabilitation in various musculoskeletal and internal diseases, including back pain, might be comparable to face-to-face rehabilitation or better than non-rehabilitation. In Germany, a standardized back school for patients with chronic back pain is provided in outpatient rehabilitation centers. The effectiveness of this standardized back school was shown in a randomized controlled trial in face-to-face rehabilitation. This study examines non-inferiority of a hybrid rehabilitation applying a digital version of the standardized back school against a rehabilitation applying the face-to-face back school.</p><p><strong>Methods/design: </strong>We recruit 320 patients in eight German outpatient rehabilitation centers. Patients are randomized equally to the intervention and control groups. Patients aged 18 to 65 years with back pain are included. Patients lacking a suitable private electronic device and German language skills are excluded. Both groups receive the standardized back school as part of the 3-week rehabilitation program. The control group receives the back school conventionally in face-to-face meetings within the outpatient rehabilitation center. The intervention group receives the back school online using a private electronic device. Besides the back school, the patients participate in rehabilitation programs according to the German rehabilitation guideline for patients with chronic back pain. Hence, the term \"hybrid\" rehabilitation for the intervention group is used. The back school consists of seven modules. We assess data at four time points: start of rehabilitation, end of rehabilitation, 3 months after the end of rehabilitation and, 12 months after the end of rehabilitation. The primary outcome is pain self-efficacy. Secondary outcomes are, amongst others, motivational self-efficacy, cognitive and behavioral pain management, and disorder and treatment knowledge. Guided interviews with patients, physicians, physiotherapists and other health experts supplement our study with qualitative data.</p><p><strong>Discussion/aim: </strong>Our randomized controlled trial aims to demonstrate non-inferiority of the online back school, compared to conventional implementation of the back school.</p><p><strong>Trial registration: </strong>German Clinical Trials Register (DRKS00028770, April 05, 2022).</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-023-00013-4.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10125254/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45972639","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}

{"title":"Mobile applications in the Philippines during the COVID-19 pandemic: systematic search, use case mapping, and quality assessment using the Mobile App Rating Scale (MARS).","authors":"Aldren Gonzales, Razel Custodio, Marie Carmela Lapitan, Mary Ann Ladia","doi":"10.1186/s44247-023-00007-2","DOIUrl":"10.1186/s44247-023-00007-2","url":null,"abstract":"<p><strong>Background: </strong>In the Philippines, various mobile health apps were implemented during the COVID-19 pandemic with very little knowledge in terms of their quality. The aims of this paper were 1) to systemically search for mobile apps with COVID-19 pandemic use case that are implemented in the Philippines; 2) to assess the apps using Mobile App Rating Scale (MARS); and 3) to identify the critical points for future improvements of these apps.</p><p><strong>Methods: </strong>To identify existing mobile applications with COVID-19 pandemic use case employed in the Philippines, Google Play and Apple App Stores were systematically searched. Further search was conducted using the Google Search. Data were extracted from the app web store profile and apps were categorized according to use cases. Mobile apps that met the inclusion criteria were independently assessed and scored by two researchers using the MARS-a 23-item, expert-based rating scale for assessing the quality of mHealth applications.</p><p><strong>Results: </strong>A total of 27 apps were identified and assessed using MARS. The majority of the apps are designed for managing exposure to COVID-19 and for promoting health monitoring. The overall MARS score of all the apps is 3.62 points (SD 0.7), with a maximum score of 4.7 for an app used for telehealth and a minimum of 2.3 for a COVID-19 health declaration app. The majority (<i>n</i> = 19, 70%) of the apps are equal to or exceeded the minimum \"acceptable\" MARS score of 3.0. Looking at the categories, the apps for raising awareness received the highest MARS score of 4.58 (SD 0.03) while those designed for managing exposure to COVID-19 received the lowest mean score of 3.06 (SD 0.6).</p><p><strong>Conclusions: </strong>There is a heterogenous quality of mHealth apps implemented during the COVID-19 pandemic in the Philippines. The study also identified areas to better improve the tools. Considering that mHealth is expected to be an integral part of the healthcare system post-pandemic, the results warrant better policies and guidance in the development and implementation to ensure quality across the board and as a result, positively impact health outcomes.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s44247-023-00007-2.</p>","PeriodicalId":72426,"journal":{"name":"BMC digital health","volume":" ","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9985954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44240935","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}