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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis

Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis

This paper has 2 primary objectives: (1) to ascertain the predictive power of linguistic features found in pain-related tweets using machine learning and (2) to use semantic network analysis to identify central concepts and themes in chronic pain–related tweets using a text-analysis software program. This paper examines how linguistic patterns in pain-related discussions on social media vary across different US states with high and low opioid mortality.

ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

J Med Internet Res 2025;27:e67506

Combining Artificial Intelligence and Human Support in Mental Health: Digital Intervention With Comparable Effectiveness to Human-Delivered Care

Combining Artificial Intelligence and Human Support in Mental Health: Digital Intervention With Comparable Effectiveness to Human-Delivered Care

A noninferiority power analysis was conducted before the retrospective analysis of external control data to estimate the total sample size needed to quantify clinical effectiveness (ie, change in GAD-7 total score) compared with an active external control. Clinical effectiveness was defined as a change in GAD-7 score over either the course of 6 treatment sessions or until recovery was reached (if sooner than 6 sessions).

Clare E Palmer, Emily Marshall, Edward Millgate, Graham Warren, Michael Ewbank, Elisa Cooper, Samantha Lawes, Alastair Smith, Chris Hutchins-Joss, Jessica Young, Malika Bouazzaoui, Morad Margoum, Sandra Healey, Louise Marshall, Shaun Mehew, Ronan Cummins, Valentin Tablan, Ana Catarino, Andrew E Welchman, Andrew D Blackwell

J Med Internet Res 2025;27:e69351

The Impact of Technology-Enabled Medical Nutrition Therapy on Weight Loss in Adults With Overweight and Obesity: Retrospective Observational Study

The Impact of Technology-Enabled Medical Nutrition Therapy on Weight Loss in Adults With Overweight and Obesity: Retrospective Observational Study

Given the retrospective and observational nature of this study, a prospective sample size and power analysis were not performed. Data used for this analysis were self-reported by participants in the app or during the appointment, where RDs entered data on the participants’ behalf. In addition, platform-collected app engagement data were used to derive the number of appointments, app touchpoints, and time between recorded weights.

Emily A Hu, Tommy Kelley, Ajay Haryani

JMIR Mhealth Uhealth 2025;13:e70228

Perspectives and Experiences With Large Language Models in Health Care: Survey Study

Perspectives and Experiences With Large Language Models in Health Care: Survey Study

Empowered consumers, informed by LLMs, may also disrupt traditional power dynamics (ie, patient and provider, teacher and student) where professionals are the traditional knowledge keepers. These consumer changes would likely necessitate acquiring new interpersonal skills to manage the demands of a more health-literate population and managing misinformed consumers.

Jennifer Sumner, Yuchen Wang, Si Ying Tan, Emily Hwee Hoon Chew, Alexander Wenjun Yip

J Med Internet Res 2025;27:e67383

Family Caregiver Perspectives on Digital Methods to Measure Stress: Qualitative Descriptive Study

Family Caregiver Perspectives on Digital Methods to Measure Stress: Qualitative Descriptive Study

We continued interview recruitment until the study team considered that no new data was being identified, and we had reached sufficient information power [33] for our relatively narrowly focused aim, participant specificity, and quality of dialogue while also not specifically conducting deductive analysis using theory and using a cross-case analysis strategy (Multimedia Appendix 3). We conducted the 6 steps of Brain and Clark reflexive thematic analysis [34].

Louise Rose, Sian Saha, Emily Flowers, Chee Siang Ang, Alexander J Casson, Joan Condell, Faith Matcham, Tony Robinson, John Rooksby

J Med Internet Res 2025;27:e66034