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Leveraging Technology to Engage Supplemental Nutrition Assistance Program Consumers With Children at Farmers Markets: Qualitative Community-Engaged Approach to App Development

Leveraging Technology to Engage Supplemental Nutrition Assistance Program Consumers With Children at Farmers Markets: Qualitative Community-Engaged Approach to App Development

We sought to raise awareness of nutrition incentive programs among SNAP consumers with children by developing a mobile app. To do this we leveraged a qualitative community-engaged process. We chose a community-engaged approach because the technology that is desired and designed by end users should be more useful to them, and it can lead to rapid adoption [30,31].

Callie Ogland-Hand, Jillian Schulte, Owusua Yamoah, Kathryn Poppe, Timothy H Ciesielski, Regan Gee, Ana Claudia Zubieta, Darcy A Freedman

JMIR Form Res 2025;9:e70104


Examining the Longitudinal Impact of Within- and Between-Day Fluctuations in Food Parenting Practices on Child Dietary Intake: Protocol for a Longitudinal Cohort Study Within a Sample of Preschooler-Parent Dyads

Examining the Longitudinal Impact of Within- and Between-Day Fluctuations in Food Parenting Practices on Child Dietary Intake: Protocol for a Longitudinal Cohort Study Within a Sample of Preschooler-Parent Dyads

After potentially eligible participants indicated an interest in the study and provided consent to be contacted, a member of the Preschool Plates cohort study team reached out to them to complete a full eligibility screening.

Katie A Loth, Julian Wolfson, Martha Barnard, Natalie Hogan, T James Brandt, Jayne A Fulkerson, Jennifer O Fisher

JMIR Res Protoc 2025;14:e73276


Personalized Digital Care Pathways Enable Enhanced Patient Management as Perceived by Health Care Professionals: Mixed-Methods Study

Personalized Digital Care Pathways Enable Enhanced Patient Management as Perceived by Health Care Professionals: Mixed-Methods Study

Thus, the main aim of this study was to characterize the perceived usefulness of a PDCP in chronic patient management. The secondary aim was to describe the number and types of decisions supported by the software, thus quantifying them as a proxy for the system’s impact on decision support and patient management efficiency. This was a mixed-methods retrospective study, conducted to evaluate the usefulness of PDCP as a CDSS for HCPs.

David Rodrigues, Clara Jasmins, Ricardo Ladeiras-Lopes, Luis Patrao, Eduardo Freire Rodrigues

JMIR Hum Factors 2025;12:e68581


Experiences Receiving and Delivering Virtual Health Care For Women: Qualitative Evidence Synthesis

Experiences Receiving and Delivering Virtual Health Care For Women: Qualitative Evidence Synthesis

Each article was assigned a richness score by 2 team members (AS and TL-D), ranging from 1 to 5, based on the volume and depth of relevant qualitative data [28]. We sampled a subset of articles for abstraction with a focus on overrepresenting marginalized voices, purposively choosing articles from each health care delivery pathway, and prioritizing articles with higher richness scores.

Karen M Goldstein, Sharron Rushton, Allison A Lewinski, Abigail Shapiro, Tiera Lanford-Davey, Jessica N Coleman, Neetu Chawla, Dhara B Patel, Katherine Van Loon, Megan Shepherd-Banigan, Catherine Sims, Sarah Cantrell, Susan Alton Dailey, Jennifer M Gierisch

J Med Internet Res 2025;27:e68314


Ethical Challenges and Opportunities of AI in End-of-Life Palliative Care: Integrative Review

Ethical Challenges and Opportunities of AI in End-of-Life Palliative Care: Integrative Review

AI is a subject within computer science that develops systems capable of performing tasks that simulate human capabilities, such as learning, reasoning, and decision-making. Machine learning (ML) allows algorithms to learn from data without explicit programming within this field. At the same time, deep learning, a subset of ML, uses advanced neural networks to analyze large volumes of information and generate accurate predictions.

Abel García Abejas, David Geraldes Santos, Fabio Leite Costa, Aida Cordero Botejara, Helder Mota-Filipe, Àngels Salvador Vergés

Interact J Med Res 2025;14:e73517


Prevalence of Multiple Chronic Conditions Among Adults in the All of Us Research Program: Exploratory Analysis

Prevalence of Multiple Chronic Conditions Among Adults in the All of Us Research Program: Exploratory Analysis

Moreover, research suggests that more contemporary generations of adults have a greater MCC burden and are diagnosed with MCC at earlier ages than previous generations [6]. Estimation of the prevalence of MCC throughout all stages of adulthood is a critical reflection of the MCC burden; it is important to examine the prevalence of MCC broadly using regularly updated data sources to inform targeted prevention and management strategies and resource prioritization.

Xintong Li, Caitlin Dreisbach, Carolina M Gustafson, Komal Patel Murali, Theresa A Koleck

JMIR Form Res 2025;9:e69138


Population-Wide Depression Incidence Forecasting Comparing Autoregressive Integrated Moving Average and Vector Autoregressive Integrated Moving Average to Temporal Fusion Transformers: Longitudinal Observational Study

Population-Wide Depression Incidence Forecasting Comparing Autoregressive Integrated Moving Average and Vector Autoregressive Integrated Moving Average to Temporal Fusion Transformers: Longitudinal Observational Study

When there was no breakpoint in between, we assumed that the validation and testing sets were a smooth extension of the training set. Models were assumed to be trained to forecast incidence in a “stable period.” An example of a stable period sample is shown in Figure 2 D, in which no breakpoint existed throughout the last 4 years (from 2011 to 2014). On the other hand, if one or more breakpoints existed in the last 4 years, such a sliding window was treated as an “unstable period” sample.

Deliang Yang, Yiyi Tang, Vivien Kin Yi Chan, Qiwen Fang, Sandra Sau Man Chan, Hao Luo, Ian Chi Kei Wong, Huang-Tz Ou, Esther Wai Yin Chan, David Makram Bishai, Yingyao Chen, Martin Knapp, Mark Jit, Dawn Craig, Xue Li

J Med Internet Res 2025;27:e67156


Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model Development and Validation

Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model Development and Validation

The outcome of this fine-tuning process is a model that provides a high level of sensitivity and specificity in classifying and differentiating various types of randomized trial publications. In our study, we initially established a baseline model for classifying publications using traditional machine learning and word embedding techniques to demonstrate the effectiveness of employing a transformer-based model in identifying publications based on nested designs.

Elaheh Aghaarabi, David Murray

JMIR Med Inform 2025;13:e63267