OBJECTIVE: This study aims to assess the prevalence of smartphone ownership, the level of comfort in providing the personal information required to use mHealth apps, and interest in using an mHealth app to access harm reduction services among PWID to guide the development of an app.
METHODS: We administered a survey to 115 PWID who were enrolled via respondent-driven sampling from July 2018 to July 2019. We examined the extent to which PWID had access to smartphones; were comfortable in providing personal information such as name, email, and address; and expressed interest in various app-based services. We measured participant characteristics (demographics, health status, and behaviors) and used binary logistic and Poisson regressions to identify independent correlates of mHealth-related variables. The primary regression outcomes included summary scores for access, comfort, and interest. The secondary outcomes included binary survey responses for individual comfort or interest components.
RESULTS: Most participants were White (74/105, 70.5%), male (78/115, 67.8%), and middle-aged (mean=41.7 years), and 67.9% (74/109) owned a smartphone. Participants reported high levels of comfort in providing personal information to use an mHealth app, including name (96/109, 88.1%), phone number (92/109, 84.4%), email (85/109, 77.9%), physical address (85/109, 77.9%), and linkage to medical records (72/109, 66.1%). Participants also reported strong interest in app-based services, including medication or sterile syringe delivery (100/110, 90.9%), lab or appointment scheduling (90/110, 81.8%), medication reminders (77/110, 70%), educational material (65/110, 59.1%), and group communication forums (64/110, 58.2%). Most participants were comfortable with the idea of home delivery of syringes (93/109, 85.3%). Homeless participants had lower access to smartphones (adjusted odds ratio [AOR] 0.15, 95% CI 0.05-0.46; P=.001), but no other participant characteristics were associated with primary outcomes. Among secondary outcomes, recent SSP use was positively associated with comfort with the home delivery of syringes (AOR 3.29, 95% CI 1.04-10.3 P=.04), and being older than 50 years was associated with an increased interest in educational materials (AOR 4.64, 95% CI 1.31-16.5; P=.02) and group communication forums (AOR 3.69, 95% CI 1.10-12.4; P=.04).
CONCLUSIONS: Our findings suggest that aside from those experiencing homelessness or unstable housing, PWID broadly have access to smartphones, are comfortable with sharing personal information, and express interest in a wide array of services within an app. Given the suboptimal access to and use of SSPs among PWID, an mHealth app has a high potential to address the harm reduction needs of this vulnerable population.
METHODS: We conducted a systematic search across four databases (EBSCOhost, PubMed, Scopus, Web of Science) for relevant studies published before August 2023. Two reviewers independently examined the articles, assessed their methodological quality, and performed data extraction.
RESULTS: A total of 23 articles met the inclusion criteria. It is found that demographic, physical movement, physical appearance, psycho-cognitive, teacher-related, and contextual factors emerged as six prominent influential factors affecting adolescent bullying behavior. Specifically, demographic factors mainly encompassed age and gender; physical movement factors primarily include physical activity, sedentary behavior, physical exercise, and sports competence; physical appearance factors primarily include being overweight, too thin, too tall, or too short; psycho-cognitive factors chiefly involved cognitive empathy, motivation, enjoyment of physical activity; teacher-related factors primarily comprised activity choices, teachers competence, controlling style, autonomy support; and contextual factors primarily cover desolate climate, perceived caring climate, strong sense of competition and winning setting.
CONCLUSION: The results indicate that bullying is a complex and multifaced behavior primarily determined by demographic, physical movement, physical appearance, psycho-cognitive, teacher-related, and contextual factors. Future studies need to enhance the diversity of research samples and comparative studies on the factors influencing bullying behavior among children and adolescents in different countries. Additionally, a more extensive range of intervention studies addressing bullying behavior among children and adolescents is warranted.
METHOD: Quantitative studies published in English until May 2023 were sought by searching seven electronic databases: Web of Science, PubMed, SPORTDiscus, CINAHL, MEDLINE, Scopus, Psychology and Behavioural Sciences Collection. This review included studies that identified participants as individuals with disabilities and reported the overall (non) compliance with the 24-HMG among children and adolescents with disabilities.
RESULTS: A total of 13 studies, involving 21,101 individuals (65.95% males), aged 6 to 21 years from 9 countries, were included in the analysis. In general, 7% (95%CI: 0.05-0.09, p
APPROACH: In this paper, we propose a novel model called radiomics-reporting network (Radioport), which incorporates text attention. This model aims to improve the interpretability of deep learning radiomics in mammographic calcification diagnosis. Firstly, it employs convolutional neural networks (CNN) to extract visual features as radiomics for multi-category classification based on Breast Imaging Reporting and Data System (BI-RADS). Then, it builds a mapping between these visual features and textual features to generate diagnostic reports, incorporating an attention module for improved clarity.
MAIN RESULTS: To demonstrate the effectiveness of our proposed model, we conducted experiments on a breast calcification dataset comprising mammograms and diagnostic reports. The results demonstrate that our model can: (i) semantically enhance the interpretability of deep learning radiomics; and, (ii) improve the readability of generated medical reports.
SIGNIFICANCE: Our interpretable textual model can explicitly simulate the mammographic calcification diagnosis process.