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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

=0.0001, P=.97) GC (ACE: Cohen d=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:

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

(C) Ten-year sub-timeseries sample set construction: segmenting the 10-year sub-timeseries (one window) according to year-by-year sliding. In the example shown for 2002-2022, there are 12 sliding windows in total. The first 7 years in each sub-timeseries is the training set, the eighth year is the validation set, and the ninth and tenth year are the testing set. In the database used in this study, we analyzed 72 sub-timeseries datasets (12 samples×6 groups) from the overall population and age subgroups.

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

Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model Development and Validation

Transformer-Based Language Models for Group Randomized Trial Classification in Biomedical Literature: Model Development and Validation

Where mi is the metric value (eg, precision, recall, F1-score) for class, ni is the number of instances in class i, C is the number of classes, and N is the total number of instances in the dataset We chose Bio Med BERT as the initial pretrained model due to its superior performance compared to other existing models on our gold standard data, publications published prior to 2021 that were identified as GRT, IRGT, and SWGRT papers using search queries.

Elaheh Aghaarabi, David Murray

JMIR Med Inform 2025;13:e63267

The Prevalence and Incidence of Suicidal Thoughts and Behavior in a Smartphone-Delivered Treatment Trial for Body Dysmorphic Disorder: Cohort Study

The Prevalence and Incidence of Suicidal Thoughts and Behavior in a Smartphone-Delivered Treatment Trial for Body Dysmorphic Disorder: Cohort Study

Relevant clinician-administered measures included the Yale-Brown Obsessive Compulsive Scale Modified for BDD (BDD-YBOCS; [26]), Mini International Neuropsychiatric Interview (MINI 7.02; [27]), and the Columbia-Suicide Severity Rating Scale (C-SSRS [28]; refer to [12] for additional measures administered). The BDD-YBOCS is the gold-standard measure of BDD symptom severity and was used to characterize the sample and track symptom severity.

Adam C Jaroszewski, Natasha Bailen, Simay I Ipek, Jennifer L Greenberg, Susanne S Hoeppner, Hilary Weingarden, Ivar Snorrason, Sabine Wilhelm

JMIR Ment Health 2025;12:e63605