PLOS digital health最新文献

Remote longitudinal studies are on the rise and promise to increase reach and reduce participation barriers in chronic disease research. However, maintaining long-term retention in these studies remains challenging. Early identification of participants with different patterns of long-term retention offers the opportunity for tailored survey adaptations. Using data from the online arm of the Swiss Multiple Sclerosis Registry (SMSR), we assessed sociodemographic, health-related, and daily-life related baseline variables against measures of long-term retention in the follow-up surveys through multivariable logistic regressions and unsupervised clustering analyses. We further explored follow-up survey completion measures against survey requirements to inform future survey designs. Our analysis included data from 1,757 participants who completed a median of 4 (IQR 2-8) follow-up surveys after baseline with a maximum of 13 possible surveys. Survey start year, age, citizenship, MS type, symptom burden and independent driving were significant predictors of long-term retention at baseline. Three clusters of participants emerged, with no differences in long-term retention outcomes revealed across the clusters. Exploratory assessments of follow-up surveys suggest possible trends in increased survey complexity with lower rates of survey completion. Our findings offer insights into characteristics associated with long-term retention in remote longitudinal studies, yet they also highlight the possible influence of various unexplored factors on retention outcomes. Future studies should incorporate additional objective measures that reflect participants' individual contexts to understand their ability to remain engaged long-term and inform survey adaptations accordingly.

The availability of quality and timely data for routine monitoring of mental, neurological and substance use (MNS) disorders is a challenge, particularly in Africa. We assessed the feasibility of using an open-source data science technology (R Shiny) to improve health data reporting in Nairobi City County, Kenya. Based on a previously used manual tool, in June 2022, we developed a digital online data capture and reporting tool using the open-source Kobo toolbox. Primary mental health care providers (nurses and physicians) working in primary healthcare facilities in Nairobi were trained to use the tool to report cases of MNS disorders diagnosed in their facilities in real-time. The digital tool covered MNS disorders listed in the World Health Organization's (WHO) Mental Health Gap Action Program Intervention Guide (mhGAP-IG). In the digital system, data were disaggregated as new or repeat visits. We linked the data to a live dynamic reproducible dashboard created using R Shiny, summarising the data in tables and figures. Between January and August 2023, 9064 cases of MNS disorders (4454 newly diagnosed, 4591 revisits and 19 referrals) were reported using the digital system compared to 5321 using the manual system in a similar period in 2022. Reporting in the digital system was real-time compared to the manual system, where reports were aggregated and submitted monthly. The system improved data quality by providing timely and complete reports. Open-source applications to report health data is feasible and acceptable to primary health care providers. The technology improved real-time data capture, reporting, and monitoring, providing invaluable information on the burden of MNS disorders and which services can be planned and used for advocacy. The fast and efficient system can be scaled up and integrated with national and sub-national health information systems to reduce manual data reporting and decrease the likelihood of errors and inconsistencies.

The Home Health Project, set on Clare Island, five kilometres off the Irish Atlantic coast, is a pilot exploration of ways in which various forms of technology can be utilised to improve healthcare for individuals living in isolated communities. The integration of digital health technologies presents enormous potential to revolutionise the accessibility of healthcare systems for those living in remote communities, allowing patient care to function outside of traditional healthcare settings. This study aims to explore the personal experiences and perspectives of participants who are using digital technologies in the delivery of their healthcare as part of the Home Health Project. Individual semi-structured interviews were conducted with nine members of the Clare Island community participating in the Home Health Project. Interviews took place in-person, in June 2023. Interviews were audio-recorded and transcribed verbatim. The data were analysed inductively using reflexive thematic analysis. To identify determinants of engagement with the Home Health Project, the data was then deductively coded to the Theoretical Domains Framework (TDF) and organised into themes. Seven of the possible 14 TDF domains were supported by the interview data as influences on engagement with the Project: Knowledge, Beliefs about capabilities, Optimism, Intentions, Environmental context and resources, Social influences and Emotion. Overall, participants evaluated the Home Health Project as being of high quality which contributed to self-reported increases in health literacy, autonomy, and feeling well supported in having their health concerns addressed. There was some apprehension related to data protection, coupled with a desire for extended training to address aspects of digital illiteracy. Future iterations can capitalise on the findings of this study by refining the technologies to reflect tailored health information, personalised to the individual user.

Food and beverage marketing on social media contributes to poor diet quality and health outcomes for youth, given their vulnerability to marketing's effects and frequent use of social media. This study benchmarked the reach and frequency of earned and paid media posts, an understudied social media marketing strategy, of food brands frequently targeting Canadian youth. The 40 food brands with the highest brand shares in Canada between 2015 and 2020 from frequently marketed food categories were determined using Euromonitor data. Digital media engagement data from 2020 were licensed from Brandwatch, a social intelligence platform, to analyze the frequency and reach of brand-related posts on Twitter, Reddit, Tumblr, and YouTube. The 40 food brands were mentioned on Twitter, Reddit, Tumblr, and YouTube a total of 16.85M times, reaching an estimated 42.24B users in 2020. The food categories with the most posts and reach were fast food restaurants (60.5% of posts, 58.1% of total reach) and sugar sweetened beverages (29.3% of posts, 37.9% of total reach). More men mentioned (2.77M posts) and were reached (6.88B users) by the food brands compared to women (2.47M posts, 5.51B users reached). The food and beverage brands (anonymized), with the most posts were fast food restaurant 2 (26.5% of the total posts), soft drink 2 (10.4% of the total posts), and fast food restaurant 6 (10.1% of the total posts). In terms of reach, the top brands were fast food restaurant 2 (33.1% of the total reach), soft drink 1 (18.1% of the total reach), and fast food restaurant 6 (12.2% of the total reach). There is a high number of posts on social media related to food and beverage brands that are popular among children and adolescents, primarily for unhealthy food categories and certain brands. The conversations online surrounding these brands contribute to the normalization of unhealthy food and beverage intake. Given the popularity of social media use amongst of children and adolescents, policies aiming to protect these vulnerable groups need to include the digital food environment.

Recent estimates report a high incidence and prevalence of HIV among men who have sex with men (MSM) in Malaysia. Mobile apps are a promising and cost-effective intervention modality to reach stigmatized and hard-to-reach populations to link them to HIV prevention services (e.g., HIV testing, pre-exposure prophylaxis, PrEP). This study assessed attitudes and preferences toward the format, content, and features of a mobile app designed to increase HIV testing and PrEP uptake among Malaysian MSM. We conducted six online focus groups between August and September 2021 with 20 MSM and 16 stakeholders (e.g., doctors, nurses, pharmacists, and NGO staff) to query. Transcripts were analyzed in Dedoose software to identify thematic content. Key themes in terms of app functions related to stylistic preferences (e.g., design, user interface), engagement strategies (e.g., reward systems, reminders), recommendations for new functions (e.g., enhanced communication options via chat, discussion forum), cost of services (e.g., PrEP), and legal considerations concerning certain features (e.g., telehealth, patient identification), minimizing privacy and confidentiality risks. Our data suggest that a tailored HIV prevention app would be acceptable among MSM in Malaysia. The findings further provide detailed recommendations for successfully developing a mobile app to improve access to HIV prevention services (e.g., HIV testing, PrEP) for optimal use among MSM in Malaysia.

Meta-researchers commonly leverage tools that infer gender from first names, especially when studying gender disparities. However, tools vary in their accuracy, ease of use, and cost. The objective of this study was to compare the accuracy and cost of the commercial software Genderize and Gender API, and the open-source gender R package. Differences in binary gender prediction accuracy between the three services were evaluated. Gender prediction accuracy was tested on a multi-national dataset of 32,968 gender-labeled clinical trial authors. Additionally, two datasets from previous studies with 5779 and 6131 names, respectively, were re-evaluated with modern implementations of Genderize and Gender API. The gender inference accuracy of Genderize and Gender API were compared, both with and without supplying trialists' country of origin in the API call. The accuracy of the gender R package was only evaluated without supplying countries of origin. The accuracy of Genderize, Gender API, and the gender R package were defined as the percentage of correct gender predictions. Accuracy differences between methods were evaluated using McNemar's test. Genderize and Gender API demonstrated 96.6% and 96.1% accuracy, respectively, when countries of origin were not supplied in the API calls. Genderize and Gender API achieved the highest accuracy when predicting the gender of German authors with accuracies greater than 98%. Genderize and Gender API were least accurate with South Korean, Chinese, Singaporean, and Taiwanese authors, demonstrating below 82% accuracy. Genderize can provide similar accuracy to Gender API while being 4.85x less expensive. The gender R package achieved below 86% accuracy on the full dataset. In the replication studies, Genderize and gender API demonstrated better performance than in the original publications. Our results indicate that Genderize and Gender API achieve similar accuracy on a multinational dataset. The gender R package is uniformly less accurate than Genderize and Gender API.

Aims: Patients visiting the emergency department (ED) or hospitalized for heart failure (HF) are at increased risk for subsequent adverse outcomes, however effective risk stratification remains challenging. We utilized a machine-learning (ML)-based approach to identify HF patients at risk of adverse outcomes after an ED visit or hospitalization using a large regional administrative healthcare data system.

Methods and results: Patients visiting the ED or hospitalized with HF between 2002-2016 in Alberta, Canada were included. Outcomes of interest were 30-day and 1-year HF-related ED visits, HF hospital readmission or all-cause mortality. We applied a feature extraction method using deep feature synthesis from multiple sources of health data and compared performance of a gradient boosting algorithm (CatBoost) with logistic regression modelling. The area under receiver operating characteristic curve (AUC-ROC) was used to assess model performance. We included 50,630 patients with 93,552 HF ED visits/hospitalizations. At 30-day follow-up in the holdout validation cohort, the AUC-ROC for the combined endpoint of HF ED visit, HF hospital readmission or death for the Catboost and logistic regression models was 74.16 (73.18-75.11) versus 62.25 (61.25-63.18), respectively. At 1-year follow-up corresponding values were 76.80 (76.1-77.47) versus 69.52 (68.77-70.26), respectively. AUC-ROC values for the endpoint of all-cause death alone at 30-days and 1-year follow-up were 83.21 (81.83-84.41) versus 69.53 (67.98-71.18), and 85.73 (85.14-86.29) versus 69.40 (68.57-70.26), for the CatBoost and logistic regression models, respectively.

Conclusions: ML-based modelling with deep feature synthesis provided superior risk stratification for HF patients at 30-days and 1-year follow-up after an ED visit or hospitalization using data from a large administrative regional healthcare system.

Health information on the Internet has a ubiquitous influence on health consumers' behaviour. Searching and evaluating online health information poses a real challenge for many health consumers. To our knowledge, our systematic review paper is the first to explore the interventions targeting lay people to improve their e-health literacy skills. Our paper aims to explore interventions to improve laypeople ability to identify trustworthy online health information. The search was conducted on Ovid Medline, Embase, Cochrane database, Academic Search Complete, and APA psych info. Publications were selected by screening title, abstract, and full text, then manual review of reference lists of selected publications. Data was extracted from eligible studies on an excel sheet about the types of interventions, the outcomes of the interventions and whether they are effective, and the barriers and facilitators for using the interventions by consumers. A mixed-methods appraisal tool was used to appraise evidence from quantitative, qualitative, and mixed-methods studies. Whittemore and Knafl's integrative review approach was used as a guidance for narrative synthesis. The total number of included studies is twelve. Media literacy interventions are the most common type of interventions. Few studies measured the effect of the interventions on patient health outcomes. All the procedural and navigation/ evaluation skills-building interventions are significantly effective. Computer/internet illiteracy and the absence of guidance/facilitators are significant barriers to web-based intervention use. Few interventions are distinguished by its implementation in a context tailored to consumers, using a human-centred design approach, and delivery through multiple health stakeholders' partnership. There is potential for further research to understand how to improve consumers health information use focusing on collaborative learning, using human-centred approaches, and addressing the social determinants of health.

Innovative information-sharing techniques and rapid access to stored research data as scientific currency have proved highly beneficial in healthcare and health research. Yet, researchers often experience conflict between data sharing to promote health-related scientific knowledge for the common good and their personal academic advancement. There is a scarcity of studies exploring the perspectives of health researchers in sub-Saharan Africa (SSA) regarding the challenges with data sharing in the context of data-intensive research. The study began with a quantitative survey and research, after which the researchers engaged in a qualitative study. This qualitative cross-sectional baseline study reports on the challenges faced by health researchers, in terms of data sharing. In-depth interviews were conducted via Microsoft Teams between July 2022 and April 2023 with 16 health researchers from 16 different countries across SSA. We employed purposive and snowballing sampling techniques to invite participants via email. The recorded interviews were transcribed, coded and analysed thematically using ATLAS.ti. Five recurrent themes and several subthemes emerged related to (1) individual researcher concerns (fears regarding data sharing, publication and manuscript pressure), (2) structural issues impacting data sharing, (3) recognition in academia (scooping of research data, acknowledgement and research incentives) (4) ethical challenges experienced by health researchers in SSA (confidentiality and informed consent, commercialisation and benefit sharing) and (5) legal lacunae (gaps in laws and regulations). Significant discomfort about data sharing exists amongst health researchers in this sample of respondents from SSA, resulting in a reluctance to share data despite acknowledging the scientific benefits of such sharing. This discomfort is related to the lack of adequate guidelines and governance processes in the context of health research collaborations, both locally and internationally. Consequently, concerns about ethical and legal issues are increasing. Resources are needed in SSA to improve the quality, value and veracity of data-as these are ethical imperatives. Strengthening data governance via robust guidelines, legislation and appropriate data sharing agreements will increase trust amongst health researchers and data donors alike.