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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

Nonpharmacological Multimodal Interventions for Cognitive Functions in Older Adults With Mild Cognitive Impairment: Scoping Review

Nonpharmacological Multimodal Interventions for Cognitive Functions in Older Adults With Mild Cognitive Impairment: Scoping Review

0.71, P=.002; MMSE: η2=0.189, P=.001) ME (ACE: Cohen d=0.64, P=.007; AVLT: η2=0.173, P=.001) PS (DRT-II: η2=0.033, P=.11) VF (Cohen d=0.73, P=.001) HEaq ATT (TMT-A and TMT-B) GC (ADAS-Cog and KMMSEar) PS (DSST) Group×time interaction ATT—TMT-A: P GC (ADAS-Cog: P=.11); KMMSE (P=.72) PS (P=.02) CT only EF (EFPT-Kas and FABat) EF (EFPT-K: η2=0.132, P CT only PT only SA only ATT (VFT-Category) GC (ADAS-Cog, CDR-SOBau, and CMMSEav) ME (list learning delayed recall test) ATT (VFT-C: χ2=23.38, P GC (ADAS-Cog: χ2=3.31

Raffy Chi-Fung Chan, Joson Hao-Shen Zhou, Yuan Cao, Kenneth Lo, Peter Hiu-Fung Ng, David Ho-Keung Shum, Arnold Yu-Lok Wong

JMIR Aging 2025;8:e70291

Population-Wide Depression Incidence Forecasting Comparing Autoregressive Integrated Moving Average and Vector Autoregressive Integrated Moving Average to Temporal Fusion Transformers: Longitudinal Observational Study

Population-Wide Depression Incidence Forecasting Comparing Autoregressive Integrated Moving Average and Vector Autoregressive Integrated Moving Average to Temporal Fusion Transformers: Longitudinal Observational Study

(D) An example of stable period sample. (E) An example of unstable period sample with sharp interruptions. (F) An example of unstable period sample with level shift. We applied Chow test breakpoint analysis [34] to detect structural breaks in the time series, which indicates whether the slopes and intercepts of outcomes in two adjacent time periods are identical [34].

Deliang Yang, Yiyi Tang, Vivien Kin Yi Chan, Qiwen Fang, Sandra Sau Man Chan, Hao Luo, Ian Chi Kei Wong, Huang-Tz Ou, Esther Wai Yin Chan, David Makram Bishai, Yingyao Chen, Martin Knapp, Mark Jit, Dawn Craig, Xue Li

J Med Internet Res 2025;27:e67156