Objectives
Individual planning techniques are frequent intervention components in physical activity (PA) promotion, but it remains underdetermined whether interpersonal regulatory efforts such as dyadic planning contribute to their success. This study examines individual planning and dyadic planning as predictors of PA in persons with pre-obesity and obesity who seek outpatient treatment for intended weight loss.
Design
Intensive-longitudinal design with 8-day daily diaries.
Methods
One hundred and twenty-seven persons with pre-obesity or obesity who consulted an outpatient endocrinology clinic took part in a correlational 8-day daily diary study. This secondary analysis used multilevel models to explain daily self-reported PA. Planning categories (no planning; dyadic planning only; both individual and dyadic planning; reference category: individual planning only) were created and entered as same-day predictors.
Results
On days with no planning, participants reported being less physically active than on days with individual planning only. While dyadic planning only did not emerge as a unique predictor of daily PA, participants were more physically active than usual when they planned both individually and dyadically as compared to planning individually only. No significant planning–PA associations emerged at the between-person level.
Discussion
Consistent with scant previous research, we found dyadic planning to be mainly a complementary strategy to individual planning. Day-to-day individual planning together with dyadic planning was linked to more PA than individual planning alone. Our findings indicate that including planning partners in PA promotion for individuals with pre-obesity and obesity intending weight loss may be promising.
The BETTER4U project (Preventing Obesity through Biologically and bEhaviorally Tailored inTERventions for You) is a Horizon Europe initiative (GAP 101080117) dedicated to advancing our understanding of the multifaceted etiology of obesity. The project aims to move beyond current knowledge of obesity-related determinants by examining the system-level interactions between biological (including genetics), lifestyle behaviors (physical activity, nutrition, sedentary behaviors), and contextual factors, including social, economic, psychological, and environmental factors. BETTER4U advances from traditional approaches by exploring determinants embedded within interconnected systems and biological frameworks. It seeks to identify and integrate polygenic risks, omics-based markers, lifestyle and contextual factors using data from biobanks and previous landmark studies. This comprehensive approach enables the refinement of Artificial Intelligence (AI) algorithms, enhancing individualization in the design of tailored interventions. The project employs real-time behavioral monitoring tools and remote technologies to track individual behaviors and metabolic responses, allowing for the development of personalized obesity prevention strategies. By doing so, BETTER4U aims to bridge the gap between research and practical application, enabling the design of interventions that are biologically and behaviorally informed. The anticipated outcomes of BETTER4U include advanced AI models that support the development of sophisticated, targeted interventions for individuals at risk of overweight or obesity. These models will provide actionable insights into how personalized lifestyle adjustments in lifestyle behaviors, such as diet and physical activity, can effectively mitigate weight gain trajectories across the lifespan. Ultimately, the project seeks to empower individuals and support precision medicine approaches in obesity prevention and treatment through active participant engagement.
This study aimed to examine the associations between personality traits, structural features of borderline personality organization, and depressive symptoms, and to test whether borderline organization dimensions mediate the links between healthy personality traits and depressive symptoms. An online survey was conducted with 709 participants (M age = 29.6; 67.6% female) who completed the Patient Health Questionnaire-9 (PHQ-9), the Borderline Personality Inventory (BPI), and the Big Five Markers Questionnaire (IPIP-BFM-50). Data were analyzed using Pearson’s correlations and a generalized linear model (GLM) approach for multiple mediation analysis, controlling for gender. Level of depressive symptoms was strongly associated with lower levels of adaptive personality traits and higher levels of structural features of borderline personality organization. Mediation analyses revealed that primitive defenses and fear of fusion consistently mediated the relationships between most personality traits (especially emotional stability) and depressive symptoms, underscoring their central role as indirect pathways of vulnerability. These findings highlight the central role of low emotional stability and associated structural features of borderline personality organization—particularly primitive defenses and fear of fusion—in shaping depressive symptoms, emphasizing key clinical targets for intervention.
As artificial intelligence (AI) technologies increasingly integrate into daily life, understanding how people perceive AI agents in trust-related interactions is critical for fostering effective human-AI collaboration. Drawing on social cognition theory, this research examines the fundamental dimensions of warmth and competence in shaping impressions and trust towards AI agents compared to humans. Across two studies, using trust-related vignettes, we investigated how warmth and competence are attributed to AI agents, human experts, and friends in various social contexts and performance outcomes. The results indicated that although both warmth and competence impact trust judgments, these traits are generally considered less important and are attributed to AI agents to a lesser extent than to humans. Moreover, AI agents were perceived as equally warm and competent, whereas humans were rated higher on both traits—especially on warmth. The findings highlight the nuanced role of social cognitive dimensions in human-AI trust, suggesting that perceptions of AI are contextdependent and affected by implicit biases. This work advances understanding of human-AI social dynamics and underscores the importance of designing AI systems that effectively balance warmth and competence to enhance trust and cooperation.
Borderline personality disorder (BPD) and eating disorders (EDs) are often comorbid and share a core feature of emotion dysregulation (EDys). While diet has been linked to mental health, its relationship with EDys and symptom severity in these groups remains understudied. This study investigated dietary intake in BPD, EDs, and their comorbidity, and examined whether EDys mediates the relationship between diet and symptom severity. Female inpatients with BPD (n = 40), ED (n = 22), and BPD with comorbid ED (BPD + ED; n = 37), along with healthy controls (HCs; n = 37) completed Food Frequency Questionnaire (FFQ-6), Emotion Dysregulation Scale (EDS), and clinical self-report measures. Dietary patterns differed between groups. Clinical groups consumed sources of omega-3 polyunsaturated fatty acids and Mediterranean diet (MD) foods less frequently than HCs. EDys fully mediated the link between dietary patterns and symptom severity in most models. The mediation was partial when omega-3 intake predicted ED severity in the ED group. Women with BPD and BPD + ED showed poorer diet quality, especially regarding omega-3 and MD-aligned foods. EDys mediated the association between low-quality diet and symptom severity, suggesting a transdiagnostic mechanism. Nutritional interventions may positively influence emotion regulation, thereby reducing the risk of developing and maintaining symptoms of BPD and EDs.
