Coronary Heart Disease and Emotional Intelligence

Emotional Intelligence in Hemodialysis Patients: The Impact of an Intradialytic Exercise Training Program

The current study aimed to investigate whether there is a relationship between emotional intelligence (EI), functional capacity, fatigue, cognitive function, and quality of life (QoL) in HD patients and to assess the effect of a 9-month intradialytic exercise training program on EI levels. Seventy-eight dialysis patients (50 M/28 F, 60.6 ± 17.2 years) participated in the cross-sectional study. Afterward, a subgroup of 18 patients (15 M/3 F, 56.7 ± 12.3 years) completed a 9-month supervised intradialytic exercise training program (three times weekly). EI was assessed by the Schutte Self Report Emotional Intelligence Test (SSEIT) and the Wong and Law Emotional Intelligence Scale (WLEIS). Functional capacity was assessed by a battery of tests. Sleep quality, depression levels, and daily sleepiness were assessed via validated questionnaires. All assessments were carried out before and after the intervention. A significant positive correlation was found between the WLEIS scores and the physical component summary of the QoL questionnaire. In contrast, the WLEIS scores were negatively associated with general and physical fatigue. The SSEIT scores were positively associated with cognitive function. After nine months of exercise training, only the group with low WLEIS scores improved their EI score significantly compared to the baseline values (98.7 ± 7.0 vs. 73.0 ± 4.0, p = 0.020), while no changes were observed in the medium or high EI groups. In conclusion, patients with higher levels of EI showed increased quality of life and lower levels of fatigue. Patients with low levels of EI are more likely to benefit from an exercise training program compared to their medium- and high-level counterparts.

Nutrition and Emotional Health Education: The Use of Emotional Intelligence and a Plant-Based Diet to Reduce Cardiometabolic Risk

Background

For individuals living with chronic conditions like diabetes mellitus and obesity, there is a need for sustainable behavioral strategies and physiologic tools. These tools support identifying and addressing barriers to healthy eating, reducing body mass index (BMI), and building increased physical resilience in real time.

Objective

To evaluate whether a 12-week learning management system designed to combine nutritional intervention with education and coaching on improving emotional intelligence (EI) could alter cardiometabolic outcomes.

Methods

This pre-post prospective study enrolled 37 adult volunteers with BMI greater than 25 to participate in a 12-week learning management system. Primary (BMI, systolic blood pressure, diastolic blood pressure, low-density lipoprotein [LDL], high-density lipoprotein, and fasting glucose levels) and secondary self-reported outcomes were assessed at baseline, 12 weeks, and 6 months after enrollment using Short Form-36, Emotional Quotient Inventory (EQi), and Whole Health Index (WHI). Linear mixed-effects regression models with random effect were used to estimate changes in primary and secondary outcomes. We adjusted for multiple testing using Holm step-down method.

Results

BMI and LDL were the only primary endpoints lower at program completion and 6-month follow-up compared to baseline levels (-1.63 and -17.77 mg/dL, respectively; P < .001). Secondary outcomes showing statistically significant improvement from baseline to 6-month follow-up included energy/fatigue (Short Form-36), self-regard (EQi), decision-making (EQi), impulse control (EQi), stress management (EQi), Whole Brain - Form A (WHI), Whole Food - Form C (WHI), and Whole Body - Form D (WHI).

Conclusion

This study provides preliminary evidence that lifestyle programs combining nutritional interventions and EI can have a significant impact on BMI and LDL. Our study highlights the potential importance of both nutrition and EI in programs targeting diet and lifestyle modification.

Bimodal affect recognition based on autoregressive hidden Markov models from physiological signals

BACKGROUND AND OBJECTIVE

Affect provides contextual information about the emotional state of a person as he/she communicates in both verbal and/or non-verbal forms. While human's are great at determining the emotional state of people while they communicate in person, it is challenging and still largely an unsolved problem to computationally determine the emotional state of a person.

METHODS

Emotional states of a person manifest in the physiological biosignals such as electrocardiogram (ECG) and electrodermal activity (EDA) because these signals are impacted by the peripheral nervous system of the body, and the peripheral nervous system is strongly coupled with the mental state of the person. In this paper, we present a method to accurately recognize six emotions using ECG and EDA signals and applying autoregressive hidden Markov models (AR-HMMs) and heart rate variability analysis on these signals. The six emotions include happiness, sadness, surprise, fear, anger, and disgust.

RESULTS

We evaluated our method on a comprehensive new dataset collected from 30 participants. Our results show that our proposed method achieves an average accuracy of 88.6% in distinguishing across the 6 emotions.

CONCLUSIONS

The key technical depth of the paper is in the use of the AR-HMMs to model the EDA signal and the use of LDA to enable accurate emotion recognition without requiring a large number of training samples. Unlike other studies, we have taken a hierarchical approach to classify emotions, where we first categorize the emotion as either positive or negative and then identify the exact emotion.

The Negative Emotions Due to Chronic Illness Screening Test (NECIS): Construct Validity in Patients with Coronary Artery Disease in Mainland China

OBJECTIVE

Patients with coronary artery disease (CAD) are susceptible to the negative emotion and its adverse influence on the disease prognosis. It is of great necessity to have a simple measurement to timely assess negative emotions in patients with CAD. The Negative Emotions due to Chronic Illness Screening Test (NECIS) is a newly developed tool to measure negative emotions. However, the construct validity has not been established yet. Therefore, the purpose of this study was to test the construct validity of the NECIS in individuals with CAD in mainland China.

METHODS

The tool was administered in a convenience sample of 376 patients with CAD hospitalized in three general hospitals in Wuhan City, China. Construct validity was evaluated through factorial validity, convergent validity and discriminant validity. Additionally, the assumed relationship between negative emotions with other associated variables (perceived control and perceived social support) was tested to provide additional evidence of the construct validity of the NECIS.

RESULTS

Exploratory factor analysis and confirmatory factor analysis established and confirmed a two-factorial structure of the NECIS. Convergent validity and discriminant validity of the NECIS were proven to be adequate. Two hypotheses regarding the relationship between negative emotions and associated variables (perceived control and perceived social support) were confirmed, which supported the satisfactory construct validity of the NECIS.

CONCLUSION

The NECIS had sound construct validity when applied to patients with CAD in mainland China. This study added new knowledge regarding the construct validity of the NECIS, which supported its psychometric properties for future use.

Emotion regulation moderates the association between chronic stress and cardiovascular disease risk in humans: a cross-sectional study

Chronic stress is a risk factor for incident cardiovascular (CV) disease. Emotion regulation is the ability to modulate one's state or behavior in response to a given situation or stressor, and may mitigate the effect of chronic stress on CV disease risk. Data from a cohort of 754 community-dwelling young to middle-aged adults who were assessed between 2007 and 2012 on stress, emotion regulation, and CV risk measures were used to test the hypothesis that emotion regulation mitigates the effect of chronic stress on CV risk. Emotion regulation was measured using the Difficulties in Emotion Regulation Scale (DERS). We created a composite stress score using data from the Cumulative Adversity Interview and the Perceived Stress Scale. Our outcomes included blood pressure, body mass index, and insulin resistance separately and combined into a composite CV risk score. Covariates included age, sex, race, years of education, and smoking status. We used multivariable logistic regression to evaluate associations between stress measures and CV risk among participants and the impact of emotion regulation (DERS scores) on this association. We found that composite stress interacted significantly with the DERS score to affect CV risk (p = .007). A median split of the DERS scores indicated that CV risk was associated with the composite stress score in the fully adjusted model (ß = 0.206; p = .005) among participants with low emotion regulation, but not among those with high emotion regulation (ß = 0.048; p = .59). Chronic stress was associated with CV risk only among participants with poor emotion regulation. Emotion regulation is a teachable skill, and may play a role in preventing CV disease.Lay summaryEmotion regulation is the ability to modify one's reaction to a negative or stressful event, and is a teachable skill. Effective emotion regulation dampens the negative effect of chronic stress on the body, which may reduce risk for cardiovascular disease.

Sub-dimensions of trait emotional intelligence and health: A critical and systematic review of the literature

Despite a growing number of studies on the role of the multidimensional construct of trait emotional intelligence (EI) in health, most have focused on global EI, without examining the role of the sub-dimensions. The present systematic review aimed to highlight the current knowledge about self-reported health associated with trait-EI sub-dimensions in general and clinical populations. We searched for the articles including valid self-report scales of trait-EI and health (mental or physical or general) in general and clinical samples. Based on 42 studies, the majority of studies was based on mental health with cross-sectional designs and the TMMS scale, in the general population. Few studies have been focused on physical health and clinical population. The description of studies results revealed that trait-EI sub-dimensions are associated to a greater extent with better mental health, rather than with physical and general health. Furthermore, intrapersonal dimensions, and especially emotion regulation, have stronger effects on health than interpersonal dimensions. Finally, patients with a clinical disorder present lower trait-EI sub-dimensions than the general population. This review supports the importance of focusing on the sub-dimensions of trait-EI to understand better the role of EI in health. The use of scales exclusively based on emotional competences in health contexts is recommended. Developing interventions targeting emotional competences according to the emotional profiles and contexts of individuals could be beneficial to improve health and disease adjustment.