JMIR Formative Research最新文献

Background: Hispanic youth in the United States have the highest rates of pediatric obesity and do not often meet national guidelines for physical activity and dietary intake. Family-based interventions can improve health outcomes in both youth and their parents and are highly relevant to Hispanics due to the cultural value of familismo (familism). However, few existing family-based obesity prevention interventions for Hispanics target adolescents and their parents, and those that do are not designed to facilitate widespread reach.

Objective: This study describes the development of Healthy Juntos (Healthy Together), a family-based intervention for Hispanic adolescents and their parents that leverages the web and smartphone technology to prevent the onset of adolescent obesity by promoting healthy lifestyle behaviors (physical activity and diet).

Methods: We used an iterative co-design process guided by the Integrate, Design, Assess, and Share (IDEAS) framework, which outlines 10 phases for developing digital interventions. Hispanic adolescents at risk for obesity and their parents (n=90; 45 dyads) participated across different phases of the intervention development process. We conducted qualitative interviews to understand their needs and preferences and to gather feedback on a series of intervention prototypes (conceptual, paper and minimally functional, and fully functional).

Results: Participants reported using technology for their health in limited ways (eg, to search for medical symptoms and recipes). They described the importance of having interactive and social features as part of a family-based digital health intervention. Their suggestions related to content, functionality, and aesthetics resulted in a fully functional prototype of a digital lifestyle intervention for Hispanic adolescents and their parents.

Conclusions: The iterative co-design process was crucial for refining the Healthy Juntos intervention. Our next steps are to evaluate its feasibility, acceptability, and preliminary effects through a pilot randomized controlled trial.

Unlabelled: To elucidate the complex relationship between spirituality and obsessive-compulsive disorder (OCD), we performed a qualitative analysis of messages (n=225) referencing spiritualities in r/OCD, a public online peer support forum for people with OCD with over 250,000 users; two central themes emerged: (1) influence of spirituality on OCD symptom manifestation and (2) impact of OCD on relationship with spirituality.

Background: Mobile (m)health interventions can expand access to and engagement in lifesaving treatment for pregnant and postpartum people with a substance use disorder (SUD). Yet, many people with lived experience and substance use providers alike are often excluded from mobile health intervention development, limiting opportunities to provide feedback on critical design components such as usability, cultural relevance, and compatibility with real-world practice.

Objective: The study engaged pregnant and postpartum people and substance use providers in formative evaluation to refine a mobile health intervention designed to support recovery.

Methods: Pregnant and postpartum participants (n=11) and providers working in recovery settings (n=13) across Missouri reviewed the same mHealth intervention. Participants completed survey and semi-structured qualitative questions on usability and compatibility after reviewing the same mHealth intervention. Survey responses and qualitative themes were compared across groups. Post hoc analyses examined differences between pregnant and postpartum participants who had used the app and those who had not (n=8) to identify barriers to participation.

Results: Both participant groups reported similar themes related to the usability and compatibility of the mobile health intervention, including a need for simplified navigation and greater personalization of app content. The e-coaching feature and directory of recovery-focused resources were viewed as valuable by both groups. Uniquely, pregnant and postpartum participants emphasized the need for app content addressing craving management, emotional triggers, and parenting stress. These participants also requested more frequent communication with the e-coach than providers recommended. Non-app users differed from app users by race, education, and household characteristics, underscoring structural barriers to engagement.

Conclusions: Engaging both pregnant and postpartum people and providers in formative evaluation reveals overlapping and distinct priorities for mHealth design. Findings highlight that user-informed development is essential for improving usability, engagement, and recovery outcomes, including reaching those least likely to engage with traditional or digital treatment supports.

Clinicaltrial:

Background: Registered dietitian nutritionists (RDNs)-referred to as registered dietitians in Japan-contribute to disease management, prevention of complications, and improvement in quality of life through individualized nutritional guidance. However, these techniques often rely on individual experience, leading to variations in quality. The nutrition care process provides a standardized framework for nutritional care, but the specific techniques used in clinical practice and their interrelationships remain unclear. Interpretive structural modeling (ISM) is a method that visualizes and hierarchically organizes interrelationships among multiple elements, making it useful for structuring complex practical skills. Therefore, clarifying the structure of nutritional guidance techniques may support the standardization of practice and the development of educational frameworks.

Objective: This study aimed to identify the elements influencing nutritional guidance techniques in clinical practice, clarify their hierarchical structure using ISM, and explore their potential applicability to the education of registered dietitians.

Methods: Three experienced RDNs participated in an expert panel. Elements influencing nutritional guidance techniques were identified through structured brainstorming and consensus-building sessions. The extracted elements were analyzed using ISM to generate a reachability matrix and derive a hierarchical structure that visualized the interrelationships among the elements.

Results: A total of 14 elements were identified and organized into a 6-level hierarchical structure. The upper levels included nutrition care process-related elements, with the "nutritional intervention plan" positioned at the top, whereas the lower levels consisted of foundational elements such as "clinical knowledge" and "understanding of patient background."

Conclusions: This study identified 14 elements influencing nutritional guidance techniques in clinical practice and systematically visualized their interrelationships as a 6-level hierarchy using ISM. The resulting model provides an initial framework that may inform the development of clinical education curricula and competency evaluation frameworks for RDNs, and it could contribute to the advancement of standardized approaches in nutritional guidance education.

Background: Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor inter- and intra-rater reliability, while gold standard diagnostic tests for aspiration, such as video fluoroscopic swallow study (VFSS) and fiberoptic endoscopic evaluation of swallowing (FEES), are cumbersome or invasive and healthcare resource intensive.

Objective: To develop and validate a novel machine learning algorithm that can analyze simple vowel phonations, to aid in predicting aspiration risk.

Methods: Recorded [i] phonations during routine nasal endoscopy from 163 unique patients were retrospectively analyzed for acoustic features including pitch, jitter, shimmer, harmonic to noise ratio (HNR), and others. Supervised machine learning (ML) was performed on the vowel phonations of those at high-risk for aspiration versus those at low-risk for aspiration. Ground truth of aspiration risk classification for model development was established using VFSS. The performance of the ML model was tested on an independent, external cohort of patient voice samples. The performance of trained Speech Language Pathologists (SLPs) to categorize high versus low-risk aspirators by listening to phonations was compared against the ML model.

Results: Mean ML risk score for those with the ground truth of high versus low aspiration risk was 0.530+ 0.310 vs 0.243+0.249, which was a significant difference (0.287, 95% CI: 0.192-0.381) p<0.001. In the development cohort, the model showed an area under the curve (AUC) for the Receiver Operator Characteristic (ROC) of 0.76 (0.67-0.84) with specificity of 0.76 and F1 score of 0.63. The performance of the model in an external testing cohort was comparable, with AUC of 0.70 (0.52-0.88) with a specificity of 0.81, and F1 score of 0.67. The ML model had comparable accuracy, sensitivity, specificity, negative and positive predictive values compared to trained SLPs in classifying aspiration risk by evaluating vowel phonations.

Conclusions: Otolaryngology (ENT) patients at high risk for aspiration have quantifiable voice characteristics that significantly differ from those who are at a low risk for aspiration, as detected by a ML model trained to analyze sustained phonation and tested on an independent cohort.

Clinicaltrial: