JMIR Formative Research最新文献

Background: In recent years, internet- and mobile-based interventions (IMIs) have become increasingly relevant in mental health care and have sparked societal debates. Psychotherapists' perspectives are essential for identifying potential opportunities for improvement, facilitating conditions, and barriers to the implementation of these interventions.

Objective: This study aims to explore psychotherapists' perspectives on opportunities for improvement, facilitating conditions, and barriers to using IMIs.

Methods: The study used a qualitative research design, utilizing open-ended items in a cross-sectional survey. A total of 350 psychotherapists were asked to provide their written opinions on various aspects of IMIs. Thematic analysis was conducted to analyze the data and identify core themes.

Results: The analysis revealed 11 core themes related to the use of IMIs, which were categorized into 4 superordinate categories: "Applicability," "Treatment Resources," "Technology," and "Perceived Risks and Barriers." While many psychotherapists viewed IMIs as a valuable support for conventional psychotherapy, they expressed skepticism about using IMIs as a substitute. Several factors were perceived as hindrances to the applicability of IMIs in clinical practice, including technological issues, subjective concerns about potential data protection risks, a lack of individualization due to the manualized nature of most IMIs, and the high time and financial costs for both psychotherapists and patients. They expressed a desire for easily accessible information on evidence and programs to reduce the time and effort required for training and advocated for this information to be integrated into the conceptualization of new IMIs.

Conclusions: The findings of this study emphasize the importance of considering psychotherapists' attitudes in the development, evaluation, and implementation of IMIs. This study revealed that psychotherapists recognized both the opportunities and risks associated with the use of IMIs, with most agreeing that IMIs serve as a tool to support traditional psychotherapy rather than as a substitute for it. Furthermore, it is essential to involve psychotherapists in discussions about IMIs specifically, as well as in the development of new methodologies in psychotherapy more broadly. Overall, this study can advance the use of IMIs in mental health care and contribute to the ongoing societal debate surrounding these interventions.

Background: Dynamic consent has the potential to address many of the issues facing traditional paper-based or electronic consent, including enrolling informed and engaged participants in the decision-making process. The Australians Together Health Initiative (ATHENA) program aims to connect participants across Queensland, Australia, with new research opportunities. At its core is dynamic consent, an interactive and participant-centric digital platform that enables users to view ongoing research activities, update consent preferences, and have ongoing engagement with researchers.

Objective: This study aimed to describe the development of the ATHENA dynamic consent platform within the framework of the ATHENA program, including how the platform was designed, its utilization by participants, and the insights gained.

Methods: One-on-one interviews were undertaken with consumers, followed by a workshop with health care staff to gain insights into the dynamic consent concept. Five problem statements were developed, and solutions were posed, from which a dynamic consent platform was constructed, tested, and used for implementation in a clinical trial. Potential users were randomly recruited from a pre-existing pool of 615 participants in the ATHENA program. Feedback on user platform experience was gained from a survey hosted on the platform.

Results: In the 13 consumer interviews undertaken, participants were positive about dynamic consent, valuing privacy, ease of use, and adequate communication. Motivators for registration were feedback on data usage and its broader community benefits. Problem statements were security, trust and governance, ease of use, communication, control, and need for a scalable platform. Using the newly constructed dynamic consent platform, 99 potential participants were selected, of whom 67 (68%) were successfully recontacted. Of these, 59 (88%) agreed to be sent the platform, 44 (74%) logged on (indicating use), and 22 (57%) registered for the clinical trial. Survey feedback was favorable, with an average positive rating of 78% across all questions, reflecting satisfaction with the clarity, brevity, and flexibility of the platform. Barriers to implementation included technological and health literacy.

Conclusions: This study describes the successful development and testing of a dynamic consent platform that was well-accepted, with users recognizing its advantages over traditional methods of consent regarding flexibility, ease of communication, and participant satisfaction. This information may be useful to other researchers who plan to use dynamic consent in health care research.

Background: Breast implants, including textured variants, have been widely used in aesthetic and reconstructive mammoplasty. However, the textured type, which is one of the shell texture types of breast implants, has been identified as a possible etiologic factor for lymphoma, specifically breast implant-associated anaplastic large cell lymphoma (BIA-ALCL). Identifying the shell texture type of the implant is critical to diagnosing BIA-ALCL. However, distinguishing the shell texture type can be difficult due to the loss of human memory and medical history. An alternative approach is to use ultrasonography, but this method also has limitations in quantitative assessment.

Objective: This study aims to determine the feasibility of using a deep learning model to classify the shell texture type of breast implants and make robust predictions from ultrasonography images from heterogeneous sources.

Methods: A total of 19,502 breast implant images were retrospectively collected from heterogeneous sources, including images captured from both Canon and GE devices, images of ruptured implants, and images without implants, as well as publicly available images. The Canon images were trained using ResNet-50. The model's performance on the Canon dataset was evaluated using stratified 5-fold cross-validation. Additionally, external validation was conducted using the GE and publicly available datasets. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (PRAUC) were calculated based on the contribution of the pixels with Gradient-weighted Class Activation Mapping (Grad-CAM). To identify the significant pixels for classification, we masked the pixels that contributed less than 10%, up to a maximum of 100%. To assess the model's robustness to uncertainty, Shannon entropy was calculated for 4 image groups: Canon, GE, ruptured implants, and without implants.

Results: The deep learning model achieved an average AUROC of 0.98 and a PRAUC of 0.88 in the Canon dataset. The model achieved an AUROC of 0.985 and a PRAUC of 0.748 for images captured with GE devices. Additionally, the model predicted an AUROC of 0.909 and a PRAUC of 0.958 for the publicly available dataset. This model maintained the PRAUC values for quantitative validation when masking up to 90% of the least-contributing pixels and the remnant pixels in breast shell layers. Furthermore, the prediction uncertainty increased in the following order: Canon (0.066), GE (0072), ruptured implants (0.371), and no implants (0.777).

Conclusions: We have demonstrated the feasibility of using deep learning to predict the shell texture type of breast implants. This approach quantifies the shell texture types of breast implants, supporting the first step in the diagnosis of BIA-ALCL.

[This corrects the article DOI: 10.2196/44592.].

Background: To monitor the use of tenofovir disoproxil fumarate and emtricitabine (TDF/FTC) and related medicines for pre-exposure prophylaxis (PrEP) as HIV prevention using commercial pharmacy data, it is necessary to determine whether TDF/FTC prescriptions are used for PrEP or for some other clinical indication.

Objective: This study aimed to validate an algorithm to distinguish the use of TDF/FTC for HIV prevention or infectious disease treatment.

Methods: An algorithm was developed to identify whether TDF/FTC prescriptions were for PrEP or for other indications from large-scale administrative databases. The algorithm identifies TDF/FTC prescriptions and then excludes patients with International Classification of Diseases (ICD)-9 diagnostic codes, medications, or procedures that suggest indications other than for PrEP (eg, documentation of HIV infection, chronic hepatitis B, or use of TDF/FTC for postexposure prophylaxis). For evaluation, we collected data by clinician assessment of medical records for patients with TDF/FTC prescriptions and compared the assessed indication identified by the clinician review with the assessed indication identified by the algorithm. The algorithm was then applied and evaluated in a large, urban, community-based sexual health clinic.

Results: The PrEP algorithm demonstrated high sensitivity and moderate specificity (99.6% and 49.6%) in the electronic medical record database and high sensitivity and specificity (99% and 87%) in data from the urban community health clinic.

Conclusions: The PrEP algorithm classified the indication for PrEP in most patients treated with TDF/FTC with sufficient accuracy to be useful for surveillance purposes. The methods described can serve as a basis for developing a robust and evolving case definition for antiretroviral prescriptions for HIV prevention purposes.