Search

Background Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions. Aims The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder. Method We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients. Results Recruitment is ongoing. Conclusions This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.

Background: Functional recovery remains a core clinical objective for patients with bipolar disorder (BD). Sociodemographic, clinical, and neurocognitive variables are associated with long‐term functional impairment, yet the impact of sex differences is unclear. Functional remediation (FR) is a validated intervention aimed at achieving functional recovery in BD. The present study assessed the effect of sex differences of FR on psychosocial functioning at post‐treatment (6‐months) and 12‐month follow‐up (FUP). To the best of our knowledge, this is the first study to explore the role of sex as a factor in the efficacy of FR. Methods: 157 participants with BD were randomly assigned to either FR (N = 77) or treatment as usual group (80). Clinical, sociodemographic, neuropsychological, and functional data were obtained using a comprehensive assessment battery. Sex differences were explored via a general linear model (GLM) for repeated measures to compare the effect of sex on the intervention over time (6 months and FUP). Results: Results demonstrated that FR benefits both sexes, males (p = 0.001; d' = 0.88) and females (p = 0.04; d' = 0.57), at 6 months suggesting a generalized functional improvement. Conversely, at 12‐month FUP sex differences were observed only in males (p = 0.005; d' = 0.68). Conclusions: FR is a beneficial intervention for males and females after treatment, suggesting that there are no relevant distinct needs. Females may benefit from ongoing psychosocial functioning booster sessions after the intervention to maintain original improvements. Future research exploring sex differences could help to identify strategies to offer personalized FR intervention approaches in individuals with BD. [ABSTRACT FROM AUTHOR]

Emotional intelligence (EI) and neurocognition (NC) impairments are common in first-episode psychosis (FEP), yet their evolution over time remains unclear. This study identified patient profiles in EI and NC performance in FEP. 98 adult FEP patients and 128 healthy controls (HCs) were tested on clinical, functional, EI, and NC variables at baseline and two-year follow-up (FUP). A repeated-measures ANOVA compared the effects of group (patients and HCs) and time on EI. Significant EI improvements were observed in both groups. Four groups were created based on NC and EI performance at baseline and FUP in patients: impairment in NC and EI, impairment in NC only, impairment in EI only, and no impairment. At FUP, patients impaired in NC and EI showed less cognitive reserve (CR), greater negative and positive symptoms, and poorer functional outcomes. At FUP, three group trajectories were identified: (I) maintain dual impairment (II) maintain no impairment or improve, (III) maintain sole impairment or worsen. The maintain dual impairment group had the lowest levels of CR. EI and NC impairments progress differently in FEP. Greater CR may protect against comorbid EI/NC impairment. Identifying these patient characteristics could contribute to the development of personalised interventions. [ABSTRACT FROM AUTHOR]

Background: Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions., Aims: The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder., Method: We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania ( n = 12), depression ( n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features ( n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients., Results: Recruitment is ongoing., Conclusions: This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.