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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

While we observed linguistic variations between HDS and LDS, the effect sizes were modest (Cohen d=0.04-0.19) [50]. Centering the study on states with the highest and lowest opioid-related death rates may inadvertently omit the multifaceted nature of chronic pain experiences throughout the United States. Aspects such as health care access, socioeconomic dynamics, and cultural nuances might influence chronic pain experiences and their linguistic expression across regions.

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

J Med Internet Res 2025;27:e67506

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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

Clinical effectiveness was quantified by calculating the change in anxiety symptoms, measured using the GAD-7, from baseline to final score, and estimating a within-participant effect size (Cohen d). A negative mean change denotes a reduction in GAD-7 total scores. Absolute Cohen d values are presented. The threshold for a clinically meaningful reduction in symptoms was defined as a change greater than the reliable change index of the GAD-7 scale (minimum of a 4-point reduction) [54].

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

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Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients

Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients

It was designed in collaboration with academic researchers of Mobilise-D and the members of the Mobilise-D Public and Patient Advisory Group (PPAG) [29]. In total, 3 rounds were developed to explore the impact of mobility and symptoms and how DMOs can be visualized. A Delphi methodology was decided upon as it allows us to reach a consensus of patient preferences with an iterative, anonymous, multistage approach with controlled feedback of comments and scores on a 5-point Likert scale [30,31].

Jack Lumsdon, Cameron Wilson, Lisa Alcock, Clemens Becker, Francesco Benvenuti, Tecla Bonci, Koen van den Brande, Gavin Brittain, Philip Brown, Ellen Buckley, Marco Caruso, Brian Caulfield, Andrea Cereatti, Laura Delgado-Ortiz, Silvia Del Din, Jordi Evers, Judith Garcia-Aymerich, Heiko Gaßner, Tova Gur Arieh, Clint Hansen, Jeffrey M Hausdorff, Hugo Hiden, Emily Hume, Cameron Kirk, Walter Maetzler, Dimitrios Megaritis, Lynn Rochester, Kirsty Scott, Basil Sharrack, Norman Sutton, Beatrix Vereijken, Ioannis Vogiatzis, Alison Yarnall, Alison Keogh, Alma Cantu

JMIR Form Res 2025;9:e68782

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