PLOS digital health最新文献

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.

The rapid advancement of digital health technologies has heightened demand for health data for secondary uses, highlighting the importance of understanding global perspectives on personal information sharing. This article examines stakeholder perceptions and attitudes toward the use of personal health data to improve personalized treatments, interventions, and research. It also identifies barriers and facilitators in health data sharing and pinpoints gaps in current research, aiming to inform ethical practices in healthcare settings that utilize digital technologies. We conducted a scoping review of peer reviewed empirical studies based on data pertaining to perceptions and attitudes towards sharing personal health data. The authors searched three electronic databases-Embase, MEDLINE, and Web of Science-for articles published (2015-2023), using terms relating to health data and perceptions. Thirty-nine articles met the inclusion criteria with sample size ranging from 14 to 29,275. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines for the design and analysis of this study. We synthesized the included articles using narrative analysis. The review captured multiple stakeholder perspectives with an up-to-date range of diverse barriers and facilitators that impact data-sharing behavior. The included studies were primarily cross-sectional and geographically concentrated in high-income settings; often overlooking diverse demographics and broader global health challenges. Most of the included studies were based within North America and Western Europe, with the United States (n = 8) and the United Kingdom (n = 7) representing the most studied countries. Many reviewed studies were published in 2022 (n = 11) and used quantitative methods (n = 23). Twenty-nine studies examined the perspectives of patients and the public while six looked at healthcare professionals, researchers, and experts. Many of the studies we reviewed reported overall positive attitudes about data sharing with variations around sociodemographic factors, motivations for sharing data, type and recipient of data being shared, consent preference, and trust.

Artificial intelligence (AI) and machine learning (ML) tools are now proliferating in biomedical contexts, and there is no sign this will slow down any time soon. AI/ML and related technologies promise to improve scientific understanding of health and disease and have the potential to spur the development of innovative and effective diagnostics, treatments, cures, and medical technologies. Concerns about AI/ML are prominent, but attention to two specific aspects of AI/ML have so far received little research attention: synthetic data and computational checklists that might promote not only the reproducibility of AI/ML tools but also increased attention to ethical, legal, and social implications (ELSI) of AI/ML tools. We administered a targeted survey to explore these two items among biomedical professionals in the United States. Our survey findings suggest that there is a gap in familiarity with both synthetic data and computational checklists among AI/ML users and developers and those in ethics-related positions who might be tasked with ensuring the proper use or oversight of AI/ML tools. The findings from this survey study underscore the need for additional ELSI research on synthetic data and computational checklists to inform escalating efforts, including the establishment of laws and policies, to ensure safe, effective, and ethical use of AI in health settings.

For orally administered drugs, palatability is key in ensuring patient acceptability and treatment compliance. Therefore, understanding children's taste sensitivity and preferences can support formulators in making paediatric medicines more acceptable. Presently, we explore if the application of computer-vision techniques to videos of children's reaction to gustatory taste strips can provide an objective assessment of palatability. Children aged 4 to 11 years old tasted four different flavoured strips: no taste, bitter, sweet, and sour. Data was collected at home, under the supervision of a guardian, with responses recorded using the Aparito Atom app and smartphone camera. Participants scored each strip on a 5-point hedonic scale. Facial landmarks were identified in the videos, and quantitative measures, such as changes around the eyes, nose, and mouth, were extracted to train models to classify strip taste and score. We received 197 videos and 256 self-reported scores from 64 participants. The hedonic scale elicited expected results: children like sweetness, dislike bitterness and have varying opinions for sourness. The findings revealed the complexity and variability of facial reactions and highlighted specific measures, such as eyebrow and mouth corner elevations, as significant indicators of palatability. This study capturing children's objective reactions to taste sensations holds promise in identifying palatable drug formulations and assessing patient acceptability of paediatric medicines. Moreover, collecting data in the home setting allows for natural behaviour, with minimal burden for participants.