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Effect of a Tailored eHealth Physical Activity Intervention on Physical Activity and Depression During Postpartum: Randomized Controlled Trial (The Postpartum Wellness Study)

Sylvia E Badon, Nina Oberman, Maya Ramsey, Charles P Quesenberry, Elaine Kurtovich, Lizeth Gomez Chavez, Susan D Brown, Cheryl L Albright, Mibhali Bhalala, Lyndsay A Avalos

JMIR Ment Health 2025;12:e64507

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Domain-Specific Evaluation of Exergame Metrics Among Older Adults With Mild Neurocognitive Disorder: Secondary Analysis of 2 Randomized Controlled Trials

Wanda Kaiser, Eling D de Bruin, Patrick Manser

JMIR Serious Games 2025;13:e65878

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Impact of the COVID-19 Pandemic and the 2021 National Institute for Health and Care Excellence Guidelines on Public Perspectives Toward Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Thematic and Sentiment Analysis on Twitter (Rebranded as X)

Similarly, topic D related to research advocacy due to the appearance of terms such as “research,” “aid,” “funding,” “NIH” (which refers to National Institutes of Health), and “CDC” (which refers to Centers for Disease Control and Prevention), the latter of which refer to public health agencies of the United States and United Kingdom that allocate research funding.

Iliya Khakban, Shagun Jain, Joseph Gallab, Blossom Dharmaraj, Fangwen Zhou, Cynthia Lokker, Wael Abdelkader, Dena Zeraatkar, Jason W Busse

J Med Internet Res 2025;27:e65087

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Assessing ChatGPT’s Capability as a New Age Standardized Patient: Qualitative Study

Joseph Cross, Tarron Kayalackakom, Raymond E Robinson, Andrea Vaughans, Roopa Sebastian, Ricardo Hood, Courtney Lewis, Sumanth Devaraju, Prasanna Honnavar, Sheetal Naik, Jillwin Joseph, Nikhilesh Anand, Abdalla Mohammed, Asjah Johnson, Eliran Cohen, Teniola Adeniji, Aisling Nnenna Nnaji, Julia Elizabeth George

JMIR Med Educ 2025;11:e63353

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Evaluating the Acceptability and Utility of a Personalized Wellness App (Aspire2B) Using AI-Enabled Digital Biomarkers: Engagement Enhancement Pilot Study

Calissa J Leslie-Miller, Shellen R Goltz, Pamela L Barrios, Christopher C Cushing, Teena Badshah, Corey T Ungaro, Shankang Qu, Yulia Berezhnaya, Tristin D Brisbois

JMIR Form Res 2025;9:e63471

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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