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Smartphone Ecological Momentary Assessment and Wearable Activity Tracking in Pediatric Depression: Cohort Study

Smartphone Ecological Momentary Assessment and Wearable Activity Tracking in Pediatric Depression: Cohort Study

Adolescent depression is a significant mental health crisis; 14.7% of the adolescent population reports at least one major depressive episode with severe impairment [1]. This trend predates the COVID-19 pandemic [2] and has continued apace [3]. The impact of adolescent depression is severe; a depressive episode leads to immediate debilitating effects plus long-term consequences [4], for example, impaired academic performance [5] and challenges in forming interpersonal relationships [6].

Jimena Unzueta Saavedra, Emma A Deaso, Margot Austin, Laura Cadavid, Rachel Kraff, Emma E M Knowles

JMIR Form Res 2025;9:e66187

Impacts of the Mindfulness Meditation Mobile App Calm on Undergraduate Students’ Sleep and Emotional State: Pilot Randomized Controlled Trial

Impacts of the Mindfulness Meditation Mobile App Calm on Undergraduate Students’ Sleep and Emotional State: Pilot Randomized Controlled Trial

The mean state depression scores pretreatment were similar in the control and treatment groups (P=.58) and represented moderate severity state depression at baseline in both groups. The mean state anxiety scores at baseline were also similar between the control and treatment groups (P=.11), however, scores represented moderate-severity state anxiety in the control group and severe state anxiety in the treatment group.

Tovan Lew, Natnaiel M Dubale, Erik Doose, Alex Adenuga, Holly E Bates, Sarah L West

JMIR Form Res 2025;9:e66131

Needs and Expectations for the myNewWay Blended Digital and Face-to-Face Psychotherapy Model of Care for Depression and Anxiety (Part 1): Participatory Design Study including People with Lived and Living Experience

Needs and Expectations for the myNewWay Blended Digital and Face-to-Face Psychotherapy Model of Care for Depression and Anxiety (Part 1): Participatory Design Study including People with Lived and Living Experience

Globally, depression and anxiety are two of the most prevalent mental health disorders. In 2019, an estimated 280 million people were living with a depressive disorder, while >300 million people were living with an anxiety disorder [1]. Depression and anxiety commonly co-occur, with comorbidity rates as high as 50% [2].

Katarina Kikas, Kathleen O'Moore, Rosemaree Kathleen Miller, Julie-Anne Therese Matheson, Sophie Li, Kathleen Varghese, Peter Baldwin, Nicole Cockayne, Alexis Estelle Whitton, Jill Maree Newby

JMIR Hum Factors 2025;12:e69499

The Effect of a Mobile App (eMOM) on Self-Discovery and Psychological Factors in Persons With Gestational Diabetes: Mixed Methods Study

The Effect of a Mobile App (eMOM) on Self-Discovery and Psychological Factors in Persons With Gestational Diabetes: Mixed Methods Study

The maternal depression was measured with the Edinburgh Postnatal Depression Scale [25] at baseline (gestational weeks 24-28) and again at gestational weeks 35-37. The scale consists of 10 items, each scored from 0 to 3, yielding a total score range of 0 to 30. A score of 10 or higher is commonly used as the threshold to indicate possible depression [25].

Sini Määttänen, Saila Koivusalo, Hanna Ylinen, Seppo Heinonen, Mikko Kytö

JMIR Mhealth Uhealth 2025;13:e60855

Correlation Between Technology and Improved Outcomes in Youth With Type 1 Diabetes Mellitus: Prospective Study Examining Outcomes for Patients With Depression and Those With Public Insurance

Correlation Between Technology and Improved Outcomes in Youth With Type 1 Diabetes Mellitus: Prospective Study Examining Outcomes for Patients With Depression and Those With Public Insurance

While some centers established a depression diagnosis based on a standard depression screener (eg, the Patient Health Questionnaire 9), other patients were asked to self-report an existing depression diagnosis. Demographic differences between patients with and without a depression diagnosis were assessed using the Welch t test and a χ2 analysis.

Natacha D Emerson, Christopher Ferber, Nicholas J Jackson, Joshua Li, Eric Tsay, Dennis Styne, Michael Gottschalk, Steven D Mittelman, Anna-Barbara Moscicki

JMIR Diabetes 2025;10:e70380

Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study

Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study

Depression is the foremost contributor to global disability [1]. Longitudinal studies show that symptoms of depression typically begin in a person’s 20s to early 30s [2]. In recent years, college students’ mental health has worsened, with major depression rising disproportionately within this population [3-5]. College students assessed during the COVID-19 pandemic witnessed a 300% increase in the risk of developing depressive disorders as compared to the previous 8 years [6].

Jessica L Borelli, Yuning Wang, Frances Haofei Li, Lyric N Russo, Marta Tironi, Ken Yamashita, Elayne Zhou, Jocelyn Lai, Brenda Nguyen, Iman Azimi, Christopher Marcotullio, Sina Labbaf, Salar Jafarlou, Nikil Dutt, Amir Rahmani

JMIR Form Res 2025;9:e67964

Efficient Online Recruitment of Patients With Depressive Symptoms Using Social Media: Cross-Sectional Observational Study

Efficient Online Recruitment of Patients With Depressive Symptoms Using Social Media: Cross-Sectional Observational Study

The following images were used (details are given in Multimedia Appendix 2): (1) a sad woman lying on the bed looking into the camera—gray theme with an orange textbox (“together against depression”); (2) a sad man sitting on the couch looking downwards—sepia theme with an orange textbox (“together against depression”); (3) a senior male physician looking into the camera with folded arms—white and blue theme with an orange textbox (“together against depression”); (4) couple 1, a couple with a woman in front

Carolin Haas, Lisa Klein, Marlene Heckl, Marija Kesić, Ann-Katrin Rueß, Jochen Gensichen, Karoline Lukaschek, Tobias Kruse, POKAL-Group

JMIR Ment Health 2025;12:e65920

Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation

Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation

People with MS may experience a variety of neurological symptoms involving the cognitive, motor, sensory, vision, bowel, or bladder domains, as well as symptoms of depression, fatigue, and sleep disturbance in their daily lives [4]. Comprehensive MS care involves timely symptom management, but clinicians’ awareness of symptoms often lags patient experience. Frequent symptom monitoring could improve clinical care and quality of life.

Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

J Med Internet Res 2025;27:e70871

The Influence of eHealth Stress Management Interventions on Psychological Health Parameters in Patients With Cardiovascular Disease: Systematic Review and Meta-Analysis

The Influence of eHealth Stress Management Interventions on Psychological Health Parameters in Patients With Cardiovascular Disease: Systematic Review and Meta-Analysis

Persistent stress diminishes quality of life over time and is linked to a higher prevalence of mental health disorders such as depression and anxiety [3,4]. Since stress may promote the development of depression and anxiety, these disorders are also considered important risk factors for the development or worsening of cardiovascular health issues [5].

Ouahiba El-Malahi, Darya Mohajeri, Alexander Bäuerle, Raluca Ileana Mincu, Christos Rammos, Christoph Jansen, Martin Teufel, Tienush Rassaf, Julia Lortz

J Med Internet Res 2025;27:e67118

Algorithmic Classification of Psychiatric Disorder–Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation

Algorithmic Classification of Psychiatric Disorder–Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation

The data used in this study comes from seven distinct subreddits: r/adhd, r/anxiety, r/bipolarreddit, r/bpd, r/depression, r/ptsd, and r/schizophrenia. Following the removal of posts containing text that would be revealing of the subreddit (see “Methods” section), there was a nearly 7-fold difference between the total number of posts in each subreddit.

Ryan Allen Shewcraft, John Schwarz, Mariann Micsinai Balan

JMIR AI 2025;4:e67369