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Scalable Precision Psychiatry With an Objective Measure of Psychological Stress: Prospective Real-World Study

Scalable Precision Psychiatry With an Objective Measure of Psychological Stress: Prospective Real-World Study

Finally, as we show here, objective data will facilitate better personalization of mental health care as recommendation methods continue to improve. Our future work within precision psychiatry may explore the impact of additional weighted scalers for the current recommendation algorithm, as well as other algorithm designs, such as indication-specific content filtering.

Helena Wang, Norman Farb, Bechara Saab

J Med Internet Res 2025;27:e56086

User and Provider Experiences With Health Education Chatbots: Qualitative Systematic Review

User and Provider Experiences With Health Education Chatbots: Qualitative Systematic Review

Personalization enhanced adoption. Chang et al [20] highlighted user desire for personalized health information, rather than generic content, to enhance relevance and usefulness. Boggiss et al [19] further supported this, noting that nearly all users wanted to customize chatbot interactions. Trust issues, particularly those related to privacy, hinder adoption.

Кyung-Eun (Anna) Choi, Sebastian Fitzek

JMIR Hum Factors 2025;12:e60205

Exploring Youth Perspectives on Digital Mental Health Platforms: Qualitative Descriptive Study

Exploring Youth Perspectives on Digital Mental Health Platforms: Qualitative Descriptive Study

To date, research suggests that reasons for lack of engagement in d MH platforms include limited in-person elements, for example, face-to-face support from a parent, peer or professional [13], privacy concerns [25], technical difficulties [25], and inadequate personalization of the platform [25,26].

Sarah Daniel, Lauren Volcko, Emilie Bassi, Julia Hews-Girard, Katherine Bright, Marianne Barker, Lia Norman, Karina Pintson, Geneca Henry, Sumaya Soufi, Chukwudumbiri Efrem Omorotionmwan, Melanie Fersovitch, Leanne Stamp, Karen Moskovic, David W Johnson, Gina Dimitropoulos

JMIR Hum Factors 2025;12:e69907

Optimizing Testimonials for Behavior Change in a Digital Intervention for Binge Eating: Human-Centered Design Study

Optimizing Testimonials for Behavior Change in a Digital Intervention for Binge Eating: Human-Centered Design Study

We asked a subset of participants if they would be willing to report personal data in the intervention to facilitate identity-based matching and personalization; all of them said yes. When you introduce that [the demographic questions] you should say: in order to really focus in on you, we’re going to ask you these questions […] I want to know you’re focusing in on me, because then it’s worth my while to continue.

Isabel R Rooper, Adrian Ortega, Thomas A Massion, Tanvi Lakhtakia, Macarena Kruger, Leah M Parsons, Lindsay D Lipman, Chidiebere Azubuike, Emily Tack, Katrina T Obleada, Andrea K Graham

JMIR Form Res 2025;9:e59691

Feature Selection for Physical Activity Prediction Using Ecological Momentary Assessments to Personalize Intervention Timing: Longitudinal Observational Study

Feature Selection for Physical Activity Prediction Using Ecological Momentary Assessments to Personalize Intervention Timing: Longitudinal Observational Study

We conclude with a practical discussion on selecting an appropriate model or ensemble and feature set, contextualizing the work around more general concerns in model training and selection for dynamic personalization based on EMA. Figure 1 shows the area under the receiver operating characteristic curve of various models in a 5-fold cross-validation on training data, and Table 1 presents their area under the curve (AUC) scores on training and test sets.

Devender Kumar, David Haag, Jens Blechert, Josef Niebauer, Jan David Smeddinck

JMIR Mhealth Uhealth 2025;13:e57255

Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study

Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study

This paper develops a data-driven algorithm that includes both passive indicators of user behavior and self-reported measures to identify individuals at a high risk of early attrition in 3 DMHIs; as such, it provides a framework that helps in the personalization of DMHIs to suit individual users based on each individual’s attrition risk. To predict attrition in DMHIs, there are 2 main considerations [18].

Sonia Baee, Jeremy W Eberle, Anna N Baglione, Tyler Spears, Elijah Lewis, Hongning Wang, Daniel H Funk, Bethany Teachman, Laura E Barnes

JMIR Ment Health 2024;11:e51567

A Digital Approach for Addressing Suicidal Ideation and Behaviors in Youth Mental Health Services: Observational Study

A Digital Approach for Addressing Suicidal Ideation and Behaviors in Youth Mental Health Services: Observational Study

This evaluation demonstrated the potential of the digital notification system to facilitate stratification and personalization of care. The results show that most young people who elicited a notification received evidence-based treatment, including brief interventions (ie, safety checks and safety plan [20-23]), and long-term psychological interventions [24-26].

Min K Chong, Ian B Hickie, Antonia Ottavio, David Rogers, Gina Dimitropoulos, Haley M LaMonica, Luke J Borgnolo, Sarah McKenna, Elizabeth M Scott, Frank Iorfino

J Med Internet Res 2024;26:e60879