PLOS digital health最新文献

In low- and middle-income countries (LMICs), reproductive health programs use mobile health (mHealth) platforms to deliver a broad range of SRH information and services to young people in rural areas. However, young people's experiences of using mobile phone platforms for SRH services in the rural contexts of LMICs remains unexplored. This review qualitatively explored the experiences and perceptions of young people's use of mobile phone platforms for SRH information and services. This qualitative evidence synthesis was conducted through a systematic search of online databases: Medline, Embase, CINAHL, PsycInfo and Scopus. We included peer reviewed articles that were conducted between 2000 to 2023 and used qualitative methods. The methodological quality of papers was assessed by two authors using Grading of Recommendations, Assessment, Development and Evaluation (GRADE) and Confidence in Evidence from Reviews of Qualitative research (CERQual) approach with the identified papers synthesized using a narrative thematic analysis approach. The 26 studies included in the review were conducted in a wide range of LMIC rural settings. The studies used seven different types of mHealth platforms in providing access to SRH information and services on contraception, family planning, sexually transmitted infections (STIs) and human immunodeficiency virus (HIV) education. Participant preferences for use of SRH service platforms centred on convenience, privacy and confidentiality, as well as ease and affordability. High confidence was found in the studies preferencing text messaging, voice messaging, and interactive voice response services while moderate confidence was found in studies focused on phone calls. The overall constraint for platforms services included poor and limited network and electricity connectivity (high confidence in the study findings), limited access to mobile phones and mobile credit due to cost, influence from socio-cultural norms and beliefs and community members (moderate confidence in the study findings), language and literacy skills constraints (high confidence in the study findings). The findings provide valuable information on the preferences of mHealth platforms for accessing SRH services among young people in rural settings in LMICs and the quality of available evidence on the topic. As such, the findings have important implications for health policy makers and implementers and mHealth technology platform developers on improving services for sustainable adoption and integration in LMIC rural health system.

Background: Telemedicine is increasingly used within healthcare worldwide. More is known about its efficacy in treating different conditions and its application to different contexts than about service-users' and practitioners' experiences or how best to support implementation.

Aims: To review adult service-users' experiences of synchronous video consultations with nurses, allied health professionals and psychological therapists, find out how consultations impact different groups of service-users and identify requirements for their conduct at individual, organisational, regional, and national levels.

Method: CINAHL, Embase, Medline, PsycINFO Scopus were searched for papers published between 01/01/2018 and 19/03/2021. One reviewer independently reviewed citations and a second reviewed those excluded by the first, in a liberal accelerated approach. Quality assessment was undertaken using the Mixed Methods Appraisal Tool and data were synthesised narratively.

Results: 65 papers were included. Service-users' experiences of video consultations ranged from feelings of connection to disconnection and ease of access to challenges to access. Many were excluded from video consultation services or research, for example because of lack of access to technology. Individual service-users required clear orientation and ongoing technical support, whereas staff needed support to develop technical and online-relational skills. At organisational levels, technology needed to be made available to users through equipment loan or service models such as hub-and-spoke; services required careful planning and integration within organisational systems; and security needed to be assured. Regional and national requirements related to interorganisational cooperation and developing functionality.

Conclusion: To support safe and equitable video consultation provision, we recommend: (1) providers and researchers consider how to maximise participation, for example through inclusive consent processes and eligibility criteria; (2) sharing video consultation user guides and technical support documentation; and (3) continuing professional development for practitioners, focusing on the technical and relational skills that service-users value, such as the ability to convey empathy online.

This article includes a literature review and a case study of artificial intelligence (AI) heart murmur detection models to analyse the opportunities and challenges in deploying AI in cardiovascular healthcare in low- or medium-income countries (LMICs). This study has two parallel components: (1) The literature review assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- and low-income countries. Reasons for the limited deployment of machine learning models are discussed, as well as model generalisation. Moreover, the literature review discusses how emerging human-centred deployment research is a promising avenue for overcoming deployment barriers. (2) A predictive AI screening model is developed and tested in a case study on heart murmur detection in rural Brazil. Our binary Bayesian ResNet model leverages overlapping log mel spectrograms of patient heart sound recordings and integrates demographic data and signal features via XGBoost to optimise performance. This is followed by a discussion of the model's limitations, its robustness, and the obstacles preventing its practical application. The difficulty with which this model, and other state-of-the-art models, generalise to out-of-distribution data is also discussed. By integrating the results of the case study with those of the literature review, the NASSS framework was applied to evaluate the key challenges in deploying AI-supported heart murmur detection in low-income settings. The research accentuates the transformative potential of AI-enabled healthcare, particularly for affordable point-of-care screening systems in low-income settings. It also emphasises the necessity of effective implementation and integration strategies to guarantee the successful deployment of these technologies.

Automatic visual recognition for photo-based food diaries is increasingly prevalent. However, existing tools in food recognition often focus on food classification and calorie counting, which may not be sufficient to support the variety of food and healthy eating goals people have. To understand how to better design computer-vision-based food diaries to support healthy eating, we began to examine how nutrition experts, such as dietitians, use the visual features of food photos to evaluate diet quality. We conducted an observation and interview study with 18 dietitians, during which we asked the dietitians to review a seven-day photo-based food diary and fill out an evaluation form about their observations, recommendations, and questions. We then conducted follow-up interviews to understand their strategies, needs, and challenges of photo diary review. Our findings show that dietitians used the photo features to understand long-term eating patterns, diet variety, eating contexts, and food portions. Dietitians also adopted various strategies to achieve these understandings, such as grouping photos to find patterns, using color to estimate food variety, and identifying background objects to infer eating contexts. These findings suggest design opportunities for future compute-vision-based food diaries to account for dietary patterns over time, incorporate contextual information in dietary analysis, and support collaborations between nutrition experts, clients, and computer vision systems in dietary review and provide individualized recommendations.

Locomotive Syndrome (LS) is defined by decreased walking and standing abilities due to musculoskeletal issues. Early diagnosis is vital as LS can be reversed with appropriate intervention. Although diagnosing LS using standardized charts is straightforward, the labor-intensive and time-consuming nature of the process limits its widespread implementation. To address this, we introduced a Deep Learning (DL)-based computer vision model that employs OpenPose for pose estimation and MS-G3D for spatial-temporal graph analysis. This model objectively assesses gait patterns through single-camera video captures, offering a novel and efficient method for LS prediction and analysis. Our model was trained and validated using a dataset of 186 walking videos, plus 65 additional videos for external validation. The model achieved an average sensitivity of 0.86, demonstrating high effectiveness in identifying individuals with LS. The model's positive predictive value was 0.85, affirming its reliable LS detection, and it reached an overall accuracy rate of 0.77. External validation using an independent dataset confirmed strong generalizability with an Area Under the Curve of 0.75. Although the model accurately diagnosed LS cases, it was less precise in identifying non-LS cases. This study pioneers in diagnosing LS using computer vision technology for pose estimation. Our accessible, non-invasive model serves as a tool that can accurately diagnose the labor-intensive LS tests using only visual assessments, streamlining LS detection and expediting treatment initiation. This significantly improves patient outcomes and marks a crucial advancement in digital health, addressing key challenges in management and care of LS.

Enrolling in a clinical trial or study requires informed consent. Furthermore, it is crucial to ensure proper consent when storing samples in biobanks for future research, as these samples may be used in studies beyond their initial purpose. For pediatric studies, consent must be obtained from both the child and their legal guardians, requiring the recording of multiple consents at once. Electronic consent has become more popular recently due to its ability to prevent errors and simplify the documentation of multiple consents. However, integrating consent capture into existing study software structures remains a challenge. This report evaluates the usability of the generic Informed Consent Service (gICS) of the University Medicine Greifswald (UMG) for obtaining electronic consent in pediatric studies. The setup was designed to integrate seamlessly with the current infrastructure and meet the specific needs of a multi-user, multi-study environment. The study was conducted in a pediatric research setting, where additional informed consent was obtained separately for the biobank. Over a period of 54 weeks, 1061 children and adolescents aged 3 to 17 years participated in the study. Out of these, 348 agreed also to participate in the biobank. The analysis included a total of 2066 consents and assents, with 945 paper-based and 1121 electronic consents. The study assessed the error susceptibility of electronic versus paper-based consents and found a significant reduction rate of errors of 94.7%. These findings provide valuable insights into the use of gICS in various studies and the practical implementation of electronic consent software in pediatric medicine.

The detection of heart disease using a stethoscope requires significant skill and time, making it expensive and impractical for widespread screening in low-resource environments. Machine learning analysis of heart sound recordings can improve upon the accessibility and accuracy of diagnoses, but existing approaches require further validation on larger and more representative clinical datasets. For many previous algorithms, segmenting the signal into its individual sound components is a key first step. However, segmentation algorithms often struggle to find S1 or S2 sounds in the presence of strong murmurs or noise that significantly alter or mask the expected sound. Segmentation errors then propagate to the subsequent disease classifier steps. We propose a novel recurrent neural network and hidden semi-Markov model (HSMM) algorithm that can both segment the signal and detect a heart murmur, removing the need for a two-stage algorithm. This algorithm formed the 'CUED_Acoustics' entry to the 2022 George B. Moody PhysioNet challenge, where it won the first prize in both the challenge tasks. The algorithm's performance exceeded that of many end-to-end deep learning approaches that struggled to generalise to new test data. As our approach both segments the heart sound and detects a murmur, it can provide interpretable predictions for a clinician. The model also estimates the signal quality of the recording, which may be useful for a screening environment where non-experts are using a stethoscope. These properties make the algorithm a promising tool for screening of abnormal heart murmurs.

The role of digital adherence technologies (DATs) in improving tuberculosis (TB) treatment adherence is an emerging area of policy discussion. Given that the directly observed therapy (DOT) has several shortcomings, alternative approaches such as DATs are vital to enhancing current practices by rendering person-centered models to support the completion of TB treatments. However, there is a lack of evidence that informs policy and program on facilitators and barriers to the uptake of DATs in the context of country-specific real-world situations. The purpose of this study was to explore the facilitators and barriers to the uptake of DATs by drawing from the accounts of people with TB (PWTB), healthcare workers (HCWs) and other key policy stakeholders in Ethiopia. A qualitative study was conducted to capture the perspectives of participants to help understand the contextual factors that are important in the uptake of DATs. The overall response from participants highlighted that uptake of DATs was high despite some critical implementation barriers. DATs were useful in reducing the burden of treatment management on both PWTB and HCWs, improving adherence and flexibility, and enhancing the patient-provider relationship. The relative simplicity of using DATs, positive feedback from important others, and current policy opportunities were seen as additional facilitators for the uptake of DATs in the Ethiopian context. Key barriers including network issues (mobile phone signals), lack of inclusivity and fear of stigma (as perceived by HCWs) were identified as key barriers that could limit the implementation of DATs. The findings of this qualitative study have provided a rich set of perspectives relevant to policymakers, providers and implementers in identifying the facilitators and barriers to the uptake of DATs in Ethiopia. The overall finding suggests that DATs are highly acceptable among the diverse categories of participants in the presence of critical barriers that limit uptake of DATs including poor infrastructure. However, key policy stakeholders believe that there are several opportunities and initiatives for feasible implementation, adaptation and scale-up of DATs in the current Ethiopian context.

Understanding users' acceptance of smoking cessation interventions features is a precursor to mobile cessation apps' uptake and use. We gauged perceptions of three features of smoking cessation mobile interventions (self-monitoring, tailored feedback and support, educational content) and their design in two smoking cessation apps, Quit Journey and QuitGuide, among young adults with low socioeconomic status (SES) who smoke. A convenience sample of 38 current cigarette smokers 18-29-years-old who wanted to quit and were non-college-educated nor currently enrolled in a four-year college participated in 12 semi-structured virtual focus group discussions on GoTo Meeting. Discussions were audio recorded, transcribed verbatim, and coded using the second Unified Theory of Acceptance and Use of Technology (UTAUT2) constructs (i.e., performance and effort expectancies, hedonic motivation, facilitating conditions, social influence), sentiment (i.e., positive, neutral, negative), and app features following a deductive thematic analysis approach. Participants (52.63% female, 42.10% non-Hispanic White) expressed positive sentiment toward self-monitoring (73.02%), tailored feedback and support (70.53%) and educational content (64.58%). Across both apps, performance expectancy was the dominant theme discussed in relation to feature acceptance (47.43%). Features' perceived usefulness centered on the reliability of apps in tracking smoking triggers over time, accommodating within- and between-person differences, and availability of on-demand cessation-related information. Skepticism about features' usefulness included the possibility of unintended consequences of self-monitoring, burden associated with user-input and effectiveness of tailored support given the unpredictable timing of cravings, and repetitiveness of cessation information. All features were perceived as easy to use. Other technology acceptance themes (e.g., social influence) were minimally discussed. Acceptance of features common to smoking cessation mobile applications among low socioeconomic young adult smokers was owed primarily to their perceived usefulness and ease of use. To increase user acceptance, developers should maximize integration within app features and across other apps and mobile devices.